Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes

Abstract

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.

Figure 1. Histo-Cytometry Workflow Pipeline

Multi-parameter confocal images of tissue sections are taken with a tiling confocal microscope (1). Images are then deconvolved (2) and compensated for fluorophore spillover (3). Voxels exhibiting specified combinations of signals in the original channels (above/below designated thresholds) are used to create a new masking channel (4), which is then used to gate/mask all other parameters of interest (5). 3D COI surfaces are constructed based on the gated signal expressed by the COI through use of semi-automatic volumetric rendering and segmentation (6). COI surface statistics are exported for quantitative analysis and phenotypic gating (7), and the identified gate thresholds are used for quantitative visualization (8). Bars represent 20 µm, unless otherwise stated.

Figure 2. Basic Immune Subset Discrimination

Irradiated CD45.1⁺ recipients were injected with a 1:99, 5:95, or 10:90 mixture of CD45.2⁺ to CD45.1⁺ donor BM and were allowed to reconstitute for 6 weeks, after which contra-lateral inguinal LN were taken for comparative analysis by flow cytometry and Histo-Cytometry (A). LN sections were stained with a panel of indicated antibodies and imaged (B). CD45.2⁺CD45.1⁻ voxels were used to create a masking channel that was utilized to further gate/mask all other imaged parameters, with the gated CD45.2 signal used for creating 3D surface renderings of CD45.2⁺ cells (C). Surface statistics were exported and plotted for identification of distinct immune subsets and compared to results obtained by flow cytometry (D). X and Y positions of CD45.2⁺ surfaces were plotted for each subset (D). Relative frequencies of identified subsets in the 1–10% chimeric animals, with (left) or without (right) inclusion of CD11c⁺ and unclassified events, were determined and compared to flow cytometry-based quantification (E). (Representative of three independent imaging quantifications)

Figure 3. Phenotypic Profiling of T cell Activation

1.5×10⁶ CD45.2⁺CD44^low OT-II, OT-I.GFP and CTB-labeled SMARTA were adoptively transferred into CD45.1⁺ recipients, which were immunized s.c. one day later with OVA-conjugated beads and CpG. Contra-lateral dLN were taken for comparative analysis by flow cytometry and Histo-Cytometry at indicated time-points (A). A CD45.2⁺CD45.1⁻ masking channel was used to gate all other parameters and the gated CD45.2 signal was used to create cell surfaces. CD45.2⁺ surface statistics were plotted for identification of OT-I, OT-II, and SMARTA populations, and were visually validated and compared to flow cytometry (B). CD45.2⁺ surfaces were phenotypically subsetted by fluorescence level s of gated CD69 and Ki-67, visually validated and compared to flow cytometry (C). Percentage of CD69⁺ and Ki-67⁺ T cell populations, as well as OT-I/II fold expansion determined by normalization to non-dividing SMARTA cells, were quantified (D). Minimum distances of OT-I/II surfaces to OVA-beads at the 38hr time-point were calculated and compared for the CD69^+/− populations (left) and plotted as a 2D contour graph for OT-II (E). N=3 for each time-point.[Ron, please indicate the meaning of the error bars in panel D]

Figure 4. DC Subset Visualization

Inguinal LN sections from C57BL/6 mice were stained with the indicated antibodies and imaged (A). CD11c⁺MHC-II⁺CD3⁻B220⁻ voxel gating allows visualization of CD11b⁺ and CD8⁺ DC (B). Original (non-gated) CD8, CD11c, MHC-II, and Lyve-1 signals provided a clear separation of the LN into discrete zones (C, left): lymphatic/medullary, inter-follicular (IFZ), T zone, and B zone, which were overlaid onto the DC-gated CD8 and CD11b image (C, right). Representative of at least three independent imaging sessions.

Figure 5. Resident DC Subset Visualization and Quantification

Inguinal BATF3.HET and BATF3.KO LN sections were stained with the indicated antibodies and imaged (A, BATF3.HET presented). CD11c⁺MHC-II⁺CD3⁻B220⁻ voxel gated CD11b and CD8 signals are displayed for BATF3.HET and BATF3.KO LN sections (B). DC surface statistics were used for identification and spatial visualization of resident CD8⁺ and CD11b⁺ DC subsets, with confirmation by flow cytometry for CD3⁻NK1.1⁻CD19⁻CD11c⁺MHC⁺ gated DC subsets (C). Frequencies of CD11c^HIGHMHC-II^INT resident CD8⁺ and CD11b⁺ DC were quantified and compared to results obtained by flow cytometry (D). Percentage of cells localized to the indicated zones within the indicated DC subsets was quantified (E). Representative of at least two independent experiments, N = 3.

Figure 6. Migratory DC Subset Visualization

Inguinal LN sections from (human-promoter) Langerin-Cre x YFP-flox reporter animals were stained with the indicated antibodies and imaged (A). Resident and migratory DC subsets were identified and spatially visualized by Histo-Cytometry (LN outline added for clarity), with confirmation by flow cytometric evaluation of CD3⁻NK1.1⁻CD19⁻CD11c⁺MHC⁺ gated DC subsets (B). Migratory DC subset compositions derived by both analytical methods were quantitatively compared (C). Distance to the LN lobe center for individual cells of distinct DC subsets were compared (D, left), and the average distance for the different populations in three individual LNs was quantified (D, right). N = 3.

Figure 7. Micro-Anatomical Separation of Distinct DC Subsets

Immunofluorescence microscopy-based (left) and cartoon (right) models of the distribution of different resident and migratory DC subsets among discrete LN micro-compartments are presented. Staining for the stromal marker ER-TR7 allows for direct visualization of LN compartmentalization into discrete micro-domains, with clear T cell zone demarcation (indicated by the CD8 stain) from the interfollicular and lymphatic/medullary regions (left). The microscopy image was mirrored and the afferent lymphatic vessels added for enhanced model clarity.