Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer
Rumana Rashid1,2,3,4, Giorgio Gaglia1,2,3, Yu-An Chen2,3
1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Insights
This study presents a new dataset of multiplexed immunofluorescence images from human tissues. The data enables high-dimensional analysis of immune cells in normal and cancerous conditions.
Area of Science:
- Immunology
- Pathology
- Bioinformatics
Background:
- Multiplexed immunofluorescence is crucial for understanding immune cell interactions in tissues.
- Formaldehyde-fixed and paraffin-embedded (FFPE) tissues are widely used but present analysis challenges.
- High-dimensional single-cell analysis requires robust imaging datasets.
Purpose of the Study:
- To document and describe a novel dataset of multiplexed immunofluorescence images.
- To provide single-cell measurements of immune lineage and other markers.
- To facilitate biological discovery and computational tool development.
Main Methods:
- Tissue cyclic immunofluorescence (t-CyCIF) was employed to generate images.
- Image artifact correction, stitching, registration, and segmentation were performed using BaSiC, ASHLAR, ilastik, and MATLAB.
- Single-cell features were extracted using HistoCAT software.
Main Results:
- A comprehensive dataset of artifact-corrected, segmented multiplexed immunofluorescence images was generated.
- Derived single-cell measurements for immune lineage and other markers are available.
- The dataset includes images from human tonsil and lung cancer FFPE tissues.
Conclusions:
- This dataset serves as a valuable resource for studying the immune system in health and disease.
- It supports the development and validation of advanced multiplexed image analysis tools.
- Enables high-dimensional, single-cell analysis for biological insights.
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.
