Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging

The diversity of innate lymphoid cells (ILCs) is rapidly expanding. Three ILC classes have emerged, ILC1, ILC2, and ILC3, with ILC1 and ILC3 including several subsets. The classification of some subsets is unclear and it remains controversial whether NK cells and ILC1 are distinct cell types. To address these issues, we analyzed ILCs and NK cells gene expression within mouse small intestine, spleen, and liver, as part of the Immunological Genome Project. Results identify unique gene-expression patterns for some ILCs and overlapping patterns between ILC1 and NK cells, whereas few ILC subsets remain indistinguishable. A transcriptional program shared by small intestine ILCs and a core ILC signature is identified. Transcripts that suggest novel ILC functions and developmental paths are revealed and discussed.

Flow cytometry allows highly quantitative analysis of complex dissociated populations at the cost of neglecting their tissue localization. In contrast, conventional microscopy methods provide spatial information, but visualization and quantification of cellular subsets defined by complex phenotypic marker combinations is challenging. Here, we describe an analytical microscopy method, "histo-cytometry," for visualizing and quantifying phenotypically complex cell populations directly in tissue sections. This technology is based on multiplexed antibody staining, tiled high-resolution confocal microscopy, voxel gating, volumetric cell rendering, and quantitative analysis. We have tested this technology on various innate and adaptive immune populations in murine lymph nodes (LNs) and were able to identify complex cellular subsets and phenotypes, achieving quantitatively similar results to flow cytometry, while also gathering cellular positional information. Here, we employ histo-cytometry to describe the spatial segregation of resident and migratory dendritic cell subsets into specialized microanatomical domains, suggesting an unexpected LN demarcation into discrete functional compartments. Copyright © 2012 Elsevier Inc. All rights reserved.

Accurate and rapid identification of cell populations is key to discovering novelty in multidimensional single cell experiments. We present a population finding algorithm X-shift that can process large datasets using fast KNN estimation of cell event density and automatically arranges populations by a marker-based classification system. X-shift analysis of mouse bone marrow data resolved the majority of known and several previously undescribed cell populations. Interestingly, previously known cell populations, as well as intermediate cell populations in early hematopoietic development, were described via novel marker combinations that were defined via routes to their locations in expressed marker space. X-shift provides a rapid, reliable approach to managed cell subset analysis that maximizes automation that not only best mimics human intuition, but as we show provides access to novel insights that “prior knowledge” might prevent the researcher from visualizing.