Abstract
Deep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. Single-cell and spatial maps confirmed that CD68+ cells were correlated with the enhanced Granzyme B expression and CD3+ cells exhibited enrichment of CD4+ phenotype in chronic tonsillitis. SpatialViz revealed morphological distributions of cellular organizations in distinct anatomical areas, spatially resolved single-cell associations across anatomical categories, and distance maps between the markers. Spatial topographic maps showed the unique organization of different tissue layers. The spatial reference framework generated network-based comparisons of multiplex data from healthy and diseased tonsils. SpatialViz is broadly applicable to multiplexed tissue biology.
Highlights
Deep molecular profiling of biological tissues is an indicator of health and disease
Imaging mass cytometry (IMC) is an emerging technology that relies on mass spectrometry and time of flight (TOF) measurements, wherein antibodies against antigens of interest are conjugated with isotopes of pure metals
The acquired imaging mass cytometry (IMC) data was first analyzed by the bottom-up approach, where a standard data analysis pipeline was used in Cell Profiler[24] and HistoCAT25 (Fig. 1a, b)
Summary
Deep molecular profiling of biological tissues is an indicator of health and disease. Several computational platforms are emerging, such as InsituNet[1,2], HMRF3, Giotto[4], Trendsceek[5], and SPARK6, to provide multiplexed imaging data analysis tools These methods compute the statistics, abundance, relationships among multiple markers, visualize marker correlations and associations as networks, graphical plots, and statistical representations. Preserving in situ cell–cell interaction and tumor microenvironments, IMC relates the metal isotope abundance to their pixel location, providing images of multiple proteins simultaneously in archived patients samples at subcellular resolution (1-μm)[18,19,20]. RNA probes and their encoding protein antibodies were conjugated to different metal isotopes to study their correlation at the population level This growing body of discoveries highlights the importance of IMC technology to reveal interdependencies among cell types and provide spatial maps for multiple subcellular resolution parameters
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