To the Editor:
Chronic rhinosinusitis (CRS), a heterogeneous disease characterized by
chronic inflammation in the nasal cavity and sinuses, causes significant
morbidity and diminished quality of life while costing the U.S. health
system $22 to 32 billion annually1. Aberrant
activation of the immune system in nasal and sinus mucosa plays a key
role in the etiology and pathophysiology of CRS. Indeed, CRS is
associated with increased expression of type 2 and other cytokines in
the tissues, and patients have been treated successfully by targeting
these molecules2. To better understand the
pathophysiologic mechanisms of CRS, characterization of the immune cells
that infiltrate nasal and sinus tissues is critical. While conventional
tools, such as flow cytometry, have been useful for identifying specific
immune cell types, the cellular complexity and functional diversity of
the human immune system necessitate the use of high-dimensional tools to
characterize heterogeneity of immune cells and to elucidate their roles
in disease3. Mass cytometry, similar to flow
cytometry, is suited to performing single-cell analysis, but with the
added advantages of minimal channel overlap and increased multiplexing
capacity. Here, we performed a proof of concept pilot study applying
mass cytometry to CRS research and characterized the T cell and lymphoid
cell populations infiltrating into nasal polyps.
Nasal polyps from 18 subjects with CRS with nasal polyps (CRSwNP) and
control sphenoid mucosal tissues from 4 subjects without CRS were
obtained (see Supplemental Table E1 for subject demographics).
Peripheral blood mononuclear cells (PBMCs) from healthy individuals
without sinus disease provided another layer of controls. Tissue cells
and PBMCs were stained with selected fluorescent-labeled antibodies or
with the mass cytometry metal-labeled T cell panel (Supplemental Table
E2). We first compared the performance of both methods using
conventional biaxial plots (Supplemental Figure E1). The ratio of
CD4+ to CD8+ cells (Figure 1A) and
proportions of CD4+, CD8+,
double-negative (DN), and double-positive (DP) cells within the
CD3+ T cell compartment (Figure 1B) were similar
within each cellular source whether analyzed by flow cytometry or by
mass cytometry.
Tissue resident memory T (TRM) cells, which highly
express the activation marker and adhesion molecule CD69, likely play a
pivotal role in pathophysiology of mucosal organs in immune-mediated
diseases4. Conventional flow cytometry revealed a
significantly greater proportion of CD4+ cells
co-expressing CD69 in nasal polyps compared to control sinus tissues
(Figure 1C, p<0.05); few CD4+ T cells in
PBMCs expressed CD69. Mass cytometry analysis showed comparable
findings. Furthermore, by mass cytometry, both nasal polyps and control
tissues contained similar proportions of CD69+CD8+ T cells (Figure 1D). Overall, by using typical
cell surface markers for T cells, such as CD4, CD8 and CD69, mass
cytometry analysis roughly corroborated flow cytometry findings.
To examine T cell subpopulations in nasal polyps more deeply, we
analyzed mass cytometry data using supervised and unsupervised data
clustering methods via the Astrolabe platform5.
Supervised clustering utilized the Human ImmunoPhenotyping Consortium
hierarchical guidelines6 adapted for the conventional
markers included in our T cell panel (Supplemental Table E2). Our panel
allowed differentiation of 11 T cell subsets (Supplemental Table E3 and
Supplemental Figure E2). Supervised clustering identified several
significant differences in subset frequencies among nasal polyps,
control sinus tissues and PBMCs. For example, both types of tissues
contained significantly lower frequencies of naïve
CD4+ T cells compared to PBMCs (Figure 1E,
p<0.01). Similarly, naïve CD8+ T cells were
significantly less frequent in nasal polyps compared to PBMCs
(p<0.05) or control sinus tissues (p<0.01). In
contrast, the proportion of CD8+ central memory T
(TCM) cells was significantly greater in nasal polyps
than in control nasal tissue (Figure 1F, p<0.01). Other
canonical T cell subsets were not notably different between the two
sources of sinus tissues (data not shown).
We next analyzed the mass cytometry data using two methods for
unsupervised clustering. A multidimensional scaling (MDS) map of 21 T
cell panel markers (Supplemental Table E2) revealed over 77 different
cell subsets, including 27 subsets in the CD4+ T cell
compartment and 23 subsets in the CD8+ T cell
compartment (Figure 2A). Examples from a nasal polyp and a control sinus
tissue are provided in Supplemental Figure E3. Comparison of multiple
nasal polyps and control sinus tissues revealed two notable differences.
First, naïve CD8+ T cells clustered into 5 subgroups
based on CD127 (IL-7R), CXCR3, and CD161 expression (Figure 2A and 2B).
Of these 5 subgroups, 4 were found in higher proportions in control
nasal tissue than in nasal polyp tissues. One of those four, namely
CD127hiCXCR3hiCD161lonaïve CD8+ cells, was significantly more prevalent in
control sinus tissue than nasal polyps (p<0.01). Second, a
subset of CD4+ TCM cells, namely
CD161hiCXCR3loCD127loCD4+ TCM cells, was present in 5 of 6
nasal polyp tissues, but was nearly absent in control sinus tissues
(Figure 2A and 2C and Supplemental Figure E3). This subset of
CD4+ TCM cells expressed CCR7, CD28,
CD45RO, CD69 and PD-1 but not CXCR5 or CD103 (Supplemental Figure E4A).
Because antigen-specific CD161+ CD4+T cells vigorously produce IL-5 and IL-13 in response to allergen
exposure7, this CD4+TCM subset may play a role in the pathophysiology of CRS
and warrants further investigation in the future.
We used viSNE, an unsupervised algorithm that generates a 2-dimensional
map of multi-dimensional data,8 to visualize mass
cytometry T cell
(CD45+CD3+CD19-cells) data (Figure 2D). Interestingly, a
CD161hiCXCR3loCD4+ T cell population (red box) similar to the
CD161hiCXCR3loCD127loCD4+ TCM cells identified by the MDS
map was detected via viSNE. By viSNE, the
CD161hiCXCR3loCD4+ population highly expressed CD69 and CD45RO but
lacked CD127 or CD103, and was more frequent in nasal polyps than in
control sinus tissues (Figure 2E, p<0.05). Heat maps showed
comparable molecular expression between
CD161hiCXCR3loCD127loCD4+ TCM cells and
CD161hiCXCR3loCD4+ T cells identified by MDS map and viSNE,
respectively (Supplemental Figure E4A and E4B). No other apparent
differences in the CD4+ T cell subsets were detected
between nasal polyps and control sinus tissues.
Finally, viSNE analysis of non-B, non-T cells
(CD45+CD3-CD19-cells) revealed a distinct cell population found in nasal polyps, but
not in control sinus tissues (Figure 2F, red circle). This innate cell
population was positive for CD45RO, CD25, CRTH2, and CD69 and expressed
low levels of CD127 and ST2 (the IL-33 receptor). Closer examination
revealed variable expression of CD25, CRTH2, ST2 and CD127 within the
population, suggesting that it may consist of several similar but
heterogeneous cell types, including group 2 innate lymphoid cells
(ILC2s)9. Further investigation, such as additional
cell surface markers, gene expression and functional assays, will be
necessary to elucidate the heterogeneity of this innate cell population
and corresponding roles in nasal polyp pathobiology.
In summary, our study demonstrates that mass cytometry is comparable to
flow cytometry for routine analysis while providing a robust capacity
for identifying unique immune cell subsets in mucosal tissues through
its high-dimensional resolution. However, several limitations must be
considered. First, this study is preliminary due to its small sample
size. As it was underpowered, likely several populations differentially
represented in nasal polyps versus control sinus tissues were missed.
Second, in-depth analysis of cytokine and transcription factor
expression and biologic functions of novel subsets, including naïve
CD8+ cells,
CD161hiCD4+ TCMcells, and ILC2-like cells, are warranted. Finally, it will be critical
to compare nasal polyps from different CRS endotypes, such as
aspirin-exacerbated respiratory disease, to fully decipher the T cell
and lymphoid cell involvement in the disease process. Further studies
using mass cytometry technology will provide an opportunity to make
major progress in clinical studies in CRS and related disorders.