Elucidating Immune Cell Changes in Celiac Disease: Revealing New Insights from Spectral Flow Cytometry

Academic Editors: Ivana Caputo and Claudio Ortolani Received: 5 December 2024 Revised: 31 January 2025 Accepted: 12 March 2025 Published: 21 March 2025 Citation: Gómez-Aguililla, S.; Farrais, S.; Senosiain, C.; LópezPalacios, N.; Arau, B.; Ruiz-Carnicer, Á.; Sánchez-Domínguez, R.; Corzo, M.; Casado, I.; Pujals, M.; et al. Elucidating Immune Cell Changes in Celiac Disease: Revealing New Insights from Spectral Flow Cytometry. Int. J. Mol. Sci. 2025,26, 2877. https://doi.org/10.3390/ ijms26072877 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Elucidating Immune Cell Changes in Celiac Disease: Revealing New Insights from Spectral Flow Cytometry Sara Gómez-Aguililla 1,†, Sergio Farrais 2,3,†, Carla Senosiain 4, Natalia López-Palacios 5, Beatriz Arau 6,7, Ángela Ruiz-Carnicer 8, Rebeca Sánchez-Domínguez 9,10, María Corzo 1, Isabel Casado 11 , Mar Pujals 6, Andrés Bodas 12, Carolina Sousa 8and Concepción Núñez 1,13,* 1Laboratorio de Investigación en Genética de Enfermedades Complejas, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain; [email protected] (S.G.-A.); [email protected] (M.C.) 2 Servicio de Aparato Digestivo, Hospital Universitario Fundación Jiménez Díaz, IIS-Fundación Jiménez Díaz, 28040 Madrid, Spain; [email protected] 3Departamento de Medicina, Universidad Autónoma, 28049 Madrid, Spain 4Servicio de Aparato Digestivo, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain; [email protected] 5Servicio de Aparato Digestivo, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain; [email protected] 6Department of Gastroenterology, Hospital Universitari Mutua Terrassa, 08221 Barcelona, Spain; [email protected] (B.A.); [email protected] (M.P.) 7Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain 8Departamento de Microbiología y Parasitología, Facultad de Farmacia, Universidad de Sevilla, 41012 Sevilla, Spain; [email protected] (Á.R.-C.); [email protected] (C.S.) 9 División de Terapias Innovadoras, CIEMAT y Unidad de Terapias Avanzadas, IIS-Fundación Jiménez Díaz y Universidad Autónoma, 28040 Madrid, Spain; [email protected] 10 Centro de Investigación Biomédica en Enfermedades Raras (CIBERER), 28029 Madrid, Spain 11 Servicio de Anatomía Patológica, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hopital Clínico San Carlos (IdISSC), 28040 Madrid, Spain; [email protected] 12 Servicio de Pediatría, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain; [email protected]g 13 Redes de Investigación Cooperativa Orientada a Resultados en Salud (RICORS), 28029 Madrid, Spain *Correspondence: [email protected]g †These authors contributed equally to this work. Abstract: Celiac disease (CD) is an immune-mediated enteropathy of the small intestine triggered by gluten ingestion. Although the small bowel is the main organ affected, peripheral blood cell alterations have also been described in CD. We aimed to investigate immunological cell patterns in the blood of treated CD patients and in response to a 3-day gluten challenge (GC). Blood samples were collected from 10 patients with CD and 8 healthy controls on a gluten-free diet at baseline and 6 days after initiating the GC. All the samples were analyzed by spectral flow cytometry using a 34-marker panel. We found that patients with CD displayed a lower proportion of memory B cells compared to healthy controls, both at baseline and post-GC. Additionally, we observed the previously reported activated gut-homing CD4 + , CD8 + , and TCR γδ+ T lymphocytes on day 6 post-GC, and found the CD8 + subpopulation to be the most readily identifiable by flow cytometry. Importantly, the CCR9 marker proved effective in enhancing the selection of these gluten-responsive T cells, offering the potential for increased diagnostic accuracy. Spectral flow cytometry involves a complex data analysis, but it offers valuable insights into previously unexplored immunological responses and enables in-depth cell characterization. Keywords: gluten-free diet; gluten challenge; spectral cytometry; T cells; B cells Int. J. Mol. Sci. 2025,26, 2877 https://doi.org/10.3390/ijms26072877 Int. J. Mol. Sci. 2025,26, 2877 2 of 19 1. Introduction Celiac disease (CD) is an immune-mediated small intestinal enteropathy triggered by dietary gluten exposure in genetically predisposed individuals [ 1 ]. The small bowel is the main organ affected, but changes in peripheral blood also appear to be involved in CD pathogenesis. In treated CD patients, it is well known that gluten-specific CD4 + T cells are detectable in blood 6 days after the initiation of a 3-day gluten challenge (GC) [ 2 ]. These cells are accompanied by a wave of activated gut-homing CD8 + and γδ+ T cells [ 3 , 4 ], which are all key elements in the pathogenesis of the disease. Gluten-specific CD4 + T cells are considered the primary drivers of CD, as they recognize gluten peptides bound to HLA-DQ2/DQ8 receptors and initiate the immunological cascade leading to villous atrophy, largely mediated by CD8 + T cells in the intestinal epithelium. The increase in the γδ+ T cell intraepithelial subpopulation is also a hallmark of CD, although its role in the disease is not yet fully understood. The detection of these T cells in blood has important implications for diagnosing CD, developing novel therapeutic approaches, and identifying gluten-immunodominant epitopes, highlighting the importance of an in-depth characterization of all these cells [5–7]. In addition, other cell types have been found to be altered in the blood of patients with CD, with some changes persisting even after starting a gluten-free diet (GFD) [8–10]. For many decades, the phenotyping of peripheral blood cells has been performed to gain insights into CD pathology and identify new biomarkers or treatments [ 11 , 12 ]. Frisullo et al. reported a decrease in the percentage of circulating CD25 + FOXP3 + CD4 + regulatory T cells (T reg) when patients started a GFD [ 13 ]. In 2017, Cook et al. found that treated CD patients had fewer circulating total memory T reg cells compared to healthy controls (HC), but higher CD39 + memory T reg cells [ 8 ]. B lymphocytes have also been extensively studied, with changes in the proportion of memory or regulatory B cells observed in patients with CD (children and adults) compared to HC [ 9 , 14 ]. Additionally, circulating monocytes and dendritic cells have been thoroughly characterized in acute CD, potential CD, treated CD, and HC to assess cell proportions or gut-homing profiles [10,15]. In recent years, developments in the field of spectral flow cytometry have enabled the simultaneous and in-depth analysis of diverse cellular changes across multiple cell types within a single sample. The inclusion of numerous markers increases the likelihood of identifying unknown cellular phenotypes [ 16 ]. It must be noted that large panels of approximately 40 markers can be handled simultaneously. Advances in flow cytometry have been accompanied by the development of new analytical methods allowing for the identification of cell subpopulations that require the simultaneous consideration of multiple markers. Automated, unsupervised analyses significantly enhance the efficiency of and capacity for discovering novel cell populations by reducing the analysis time, minimizing subjectivity and bias, and improving reproducibility [ 17 ]. This makes spectral flow cytometry a powerful tool for understanding differences in the immune response between different groups of individuals by studying a wide range of markers, including those related to activation and homing. In the context of a 3-day GC, this technology may offer insights into how cell populations react to gluten re-exposure and unravel the mechanisms driving CD pathogenesis. We aimed to identify, by spectral flow cytometry, the differences in the immunological cell patterns between patients with CD and HC on a GFD and to explore changes in their cell responses on day 6 following a 3-day gluten reintroduction. We also attempted to improve the characterization of CD4 + , CD8 + , and TCR γδ+ T lymphocytes mobilized in the peripheral blood in response to a 3-day GC. Int. J. Mol. Sci. 2025,26, 2877 3 of 19 2. Results A total of 10 patients with CD (women: 78%; mean age: 44.11 ± 5.61 years; time on a GFD: 30.22 ± 7.42 months) and 8 HC (women: 56%; mean age: 34.11 ± 4.09 years; time on a GFD: 1 ±0 months) were included in the study. All the major immunological cell populations were accurately identified through manual gating and comprised CD4 + , CD8 + , and TCR γδ+ T lymphocytes; B lymphocytes; monocytes; dendritic cells; basophils; and innate lymphoid cells (Figure 1). Cell populations that needed the CD56 marker to be identified were excluded from the analysis due to technical issues relative to the expression of CD56 detected during the experiment. Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 3 of 20 to improve the characterization of CD4 + , CD8 + , and TCRγδ + T lymphocytes mobilized in the peripheral blood in response to a 3-day GC. 2. Results A total of 10 patients with CD (women: 78%; mean age: 44.11 ± 5.61 years; time on a GFD: 30.22 ± 7.42 months) and 8 HC (women: 56%; mean age: 34.11 ± 4.09 years; time on a GFD: 1 ± 0 months) were included in the study. All the major immunological cell populations were accurately identified through manual gating and comprised CD4 + , CD8 + , and TCRγδ + T lymphocytes; B lymphocytes; monocytes; dendritic cells; basophils; and innate lymphoid cells (Figure 1). Cell populations that needed the CD56 marker to be identified were excluded from the analysis due to technical issues relative to the expression of CD56 detected during the experiment. Figure 1. Flow cytometry gating strategy used to distinguish major immune cell populations, including CD4 + T cells, CD8 + T cells, TCR γδ+ T cells, B cells, monocytes, dendritic cells, basophils, and innate lymphoid cells (ILCs). Int. J. Mol. Sci. 2025,26, 2877 4 of 19 2.1. Immunological Cell Patterns The 70 clusters identified in the CD45 + subset using Uniform Manifold Approximation and Projection (UMAP) combined with Flow Cytometry Self-Organizing Mapping (Flow-SOM) are shown in Figure 2A. The distribution of the major cell types across the dimensionality reduction is included in Appendix A. After the edgeR and SAM algorithms, five significant clusters were identified from the analysis of CD45 + cells (Figure 2B), all corresponding to the lymphoid lineage. Two corresponded to differences between the groups (CD vs. HC): Clu-29 at baseline and Clu-68 on day 6. Two clusters, Clu-05 and Clu-69, corresponded to differences across time points within the HC group. Finally, Clu-04 showed differences between the groups on day 6 and within the CD group across time points. The relative abundance of each cluster in the groups of interest, expressed as a percentage of the total CD45 + cells analyzed, is shown in Figure 2C. To characterize the major cell populations within each cluster, we performed manual gating based on the markers expressed by the majority of cells. The manually selected populations are shown in Figure 2D, overlaid with their corresponding clusters. Clu-29, Clu-68, Clu-05, and Clu-69 showed a great overlap. However, Clu-04 showed less overlap. This cluster included a previously described minority population [ 18 ]. The defining markers of this population were the only ones that showed little to no variation in expression, and we attributed the observed differences in Clu-04 to the presence of this specific subset. Figure 2E shows the significant clusters along with the gradient of marker expression across each one. Following the same analytical steps, the CD3 + analyses are presented in Figure 3. The distribution of the 40 clusters across the dimensionality reduction is shown in Figure 3A and the expression of the markers allowing for the identification of the representative populations is shown in Appendix A. Only two clusters, Clu-21 and Clu-22, showed significant differences (Figure 3B). These differences were observed when comparing baseline to day 6 samples within the CD group and when comparing CD vs. HC on day 6 (Figure 3C). The overlap between clusters and manually gated populations in the CD3 + analysis is shown in Figure 3D. Interestingly, the markers used for the manual selection of Clu-21 were the same as those used to manually identify Clu-04 in the CD45 + analysis. Concordantly, the manually gated population corresponding to Clu-21 showed less overlap and seemed to be the previously described minority population [ 18 ]. In contrast, the two populations identified in Clu-22 had a higher overlap in manual gating. Clu-21 corresponds to CD4 + T cells, while Clu-22 includes two lineage markers (CD8 + and TCR γδ+ ). Both clusters share the expression of CD49d, β 7, CCR9, and CXCR3, which are all associated with intestinal trafficking, as well as the activation marker CD38. All the populations were characterized by the absence of PD-L1 (immune checkpoint marker), the chemokine receptors CCR4 and CX3CR1, CD69 (early activation/tissue residency), and CLA (associated with skin homing). Each cluster primarily comprised cell populations previously identified in patients with CD on day 6 following a 3-day GC (Figure 4) [ 4 , 5 ]. Specifically, CD103 and HLA-DR either showed reduced expression or were completely absent in Clu-21, which predominantly consisted of memory CD4 + T cells homing to the lamina propria, as indicated by the expression patterns of CD45RA − CCR7 − and CD49d + β 7 hi CD103 − , respectively. In contrast, Clu-22 also expressed β 7 and CD103, indicating their migration to the intestinal epithelium, along with the activation marker HLA-DR. These clusters differed in their level of expression of CCR7 and the immune checkpoint marker PD-1, with CCR7 being almost absent in Clu-21 and PD-1 being highly expressed in this cluster (Figure 3E). Int. J. Mol. Sci. 2025,26, 2877 5 of 19 Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 5 of 20 PD-1, with CCR7 being almost absent in Clu-21 and PD-1 being highly expressed in this cluster (Figure 3E). Figure 2. Unsupervised analysis of CD45 + cells. (A) Overlay of the 70 clusters generated by FlowSOM in the dimensionality reduction. (B) Clusters mapped by group and time point, highlighting the five significant clusters identified and their location in the dimensionality reduction. (C) Boxplots display- Int. J. Mol. Sci. 2025,26, 2877 6 of 19 ing significant comparisons (p< 0.05) between groups at each time point (CD: celiac disease; HC: healthy controls) and between time points (baseline vs. day 6) within each group. (D) Dimensionality reduction plot showing the overlap between the significant clusters identified by the unsupervised analyses and the manually gated populations. (E) Visualization of the significant clusters with their relative expression of cell lineage and phenotype markers. Lineage markers that were expressed and contributed to the identification of the representative population of each cluster are presented, along with all markers related to cell trafficking, cell nature, activation, and immune checkpoints. Table 1summarizes the markers used for manual gating in each analysis. Table 1. Significant clusters identified through the unsupervised analysis and the markers used for their manual gating. Analysis Cluster Lineage Phenotypic Markers CD45+ Clu-29 CD8+CD3+CD127+CD27+CD45RA+CCR7+ Clu-68 CD20+CD19+CD27+CD45RA+HLA-DR+CD49d+ Clu-04 CD4+CD3+CD38+CD49d+β7+ Clu-05 CD4+CD3+CD127+CD27+CD45RA+CCR7+ Clu-69 CD20+CD19+CCR7+CD45RA+HLA-DR+IgD+ CD3+ Clu-21 CD4+CD38+CD49d+β7+ Clu-22 CD8+CD38+CD103+β7+ Thus, validation by manual gating confirmed four clusters with significant differences between groups or time points, with two clusters corresponding to each cell subset (Clu-68 and Clu-04 in the CD45 + approach and Clu-21 and Clu-22 in the CD3 + approach). As noted earlier, Clu-04 from CD45 + and Clu-21 from CD3 + exhibited the same marker expression, and we concluded that they identified the same cell subset. Therefore, we considered that the three unique clusters represented true differences between the analyzed groups or time points. These three clusters corresponded to four different populations (CD20 + CD19 + , CD4+, CD8+, and TCRγδ+T cells), as referred to hereafter. The differences between patients with CD and HC in CD20 + CD19 + were confirmed at both time points when the CD27 + CD45RA + HLA-DR + CD49d + CD19 + CD20 + cell population was manually gated. The percentage of CD27 + CD45RA + HLA-DR + CD49d + CD20 + CD19 + cells relative to the total CD20 + CD19 + cells was significantly lower in CD. At baseline, this percentage was 23.57 ± 1.99% in patients with CD vs. 38.64 ± 3.73% in HC (p= 0.0017), and on day 6, it was 22.8 ± 1.77% in patients with CD vs. 39.2 ± 4% in HC (p= 0.0010). These results are shown in Figure 4. This population corresponded to B lymphocytes with the memory phenotype (CD27 + ). A detailed examination of markers with variable expressions such as CD39, to evaluate activation or regulatory functions, or IgD, to classify into class-switched and non-class-switched memory B cells, did not provide additional insights to increase the differences between groups. Moreover, excluding the HLA-DR selection did not alter the results, as this marker is fully represented on B lymphocytes. No differences among the proportion of total B lymphocytes were found. Int. J. Mol. Sci. 2025,26, 2877 7 of 19 Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 7 of 20 Figure 3. Unsupervised analysis of CD3 + cells. (A) Overlay of the 40 clusters generated by FlowSOM in the dimensionality reduction. (B) Clusters mapped by group and time point, highlighting the two significant clusters identified. (C) Boxplots showing significant comparisons (p< 0.05) between Int. J. Mol. Sci. 2025,26, 2877 8 of 19 groups on day 6 (CD: celiac disease; HC: healthy controls) and between time points (baseline vs. day 6) in the CD group. (D) Dimensionality reduction showing the overlap between the significant clusters and the manually gated populations. (E) Visualization of the significant clusters with their relative expression of T cell lineage and phenotype markers. Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 8 of 20 Figure 3. Unsupervised analysis of CD3 + cells. (A) Overlay of the 40 clusters generated by FlowSOM in the dimensionality reduction. (B) Clusters mapped by group and time point, highlighting the two significant clusters identified. (C) Boxplots showing significant comparisons (p < 0.05) between groups on day 6 (CD: celiac disease; HC: healthy controls) and between time points (baseline vs. day 6) in the CD group. (D) Dimensionality reduction showing the overlap between the significant clusters and the manually gated populations. (E) Visualization of the significant clusters with their relative expression of T cell lineage and phenotype markers. Figure 4. Percentage of the CD27 + HLA-DR + CD45RA + CD20 + CD19 + cells with respect to the total CD20 + CD19 + cells in patients with celiac disease (CD) and healthy controls (HC) on a gluten-free diet (baseline) and on day 6 following a 3-day gluten challenge. The predominant cell populations in Clu-04 in the CD45 + analysis and Clu-21 and Clu-22 in the CD3 + analysis have been previously described in patients with CD on day 6 following a 3-day GC [3–5]. Hereafter, they are referred to as activated gut-homing T cells. Consistently with previous reports, we observed significant differences between the baseline and day 6 within the CD group, as well as between patients with CD and HC on day 6 when they are manually gated (Figure 5). The markers used to identify these cell populations in peripheral blood were previously defined as β7 hi CD103 + CD38 + for CD8 + and TCRγδ + T cells [2,3,5], and we used β7 + CD49d + CD38 + for CD4 + T cells [18]. Using these gating strategies, we confirmed significant differences. The percentage of β7 + CD103 + CD38 + CD8 + T cells relative to the total CD8 + T cell population was significantly higher on day 6 compared to baseline in patients with CD (p = 0.002), as well as when comparing CD and HC participants on day 6 (p < 0.0001). Identical paIerns were observed in β7 + CD103 + CD38 + TCRγδ + and β7 + CD49d + CD38 + CD4 + T cells: baseline vs. day 6 in patients with CD (p = 0.009 and p = 0.0137, respectively), as well as between patients with CD and HC on day 6 (p < 0.0001 and p = 0.001, respectively). While all T cell subsets exhibited an increase in the percentage of activated gut-homing T cells, this effect was most pronounced in the CD8⁺ and TCRγδ + subsets (Figure 6). The differences between patients with CD and HC in the four cell populations considered did not seem to be influenced by sex or age (Appendix B). Figure 4. Percentage of the CD27 + HLA-DR + CD45RA + CD20 + CD19 + cells with respect to the total CD20 + CD19 + cells in patients with celiac disease (CD) and healthy controls (HC) on a gluten-free diet (baseline) and on day 6 following a 3-day gluten challenge. The predominant cell populations in Clu-04 in the CD45 + analysis and Clu-21 and Clu-22 in the CD3 + analysis have been previously described in patients with CD on day 6 following a 3-day GC [ 3 – 5 ]. Hereafter, they are referred to as activated gut-homing T cells. Consistently with previous reports, we observed significant differences between the baseline and day 6 within the CD group, as well as between patients with CD and HC on day 6 when they are manually gated (Figure 5). The markers used to identify these cell populations in peripheral blood were previously defined as β 7 hi CD103 + CD38 + for CD8 + and TCR γδ+ T cells [ 2 , 3 , 5 ], and we used β 7 + CD49d + CD38 + for CD4 + T cells [ 18 ]. Using these gating strategies, we confirmed significant differences. The percentage of β 7 + CD103 + CD38 + CD8 + T cells relative to the total CD8 + T cell population was significantly higher on day 6 compared to baseline in patients with CD (p= 0.002), as well as when comparing CD and HC participants on day 6 (p< 0.0001). Identical patterns were observed in β 7 + CD103 + CD38 + TCR γδ+ and β 7 + CD49d + CD38 + CD4 + T cells: baseline vs. day 6 in patients with CD (p= 0.009 and p= 0.0137 , respectively), as well as between patients with CD and HC on day 6 (p< 0.0001 and p= 0.001, respectively). While all T cell subsets exhibited an increase in the percentage of activated gut-homing T cells, this effect was most pronounced in the CD8 + and TCR γδ + subsets (Figure 6). The differences between patients with CD and HC in the four cell populations considered did not seem to be influenced by sex or age (Appendix B). Int. J. Mol. Sci. 2025,26, 2877 9 of 19 Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 9 of 20 Figure 5. Percentage of activated gut-homing T cells in relation to the total CD4 + , CD8 + , and TCRγδ + corresponding to patients with celiac disease (CD) and healthy controls (HC) on a gluten-free diet (baseline) and on day 6 following a 3-day gluten challenge. Only significant p-values are shown. Figure 5. Percentage of activated gut-homing T cells in relation to the total CD4 + , CD8 + , and TCR γδ+ corresponding to patients with celiac disease (CD) and healthy controls (HC) on a gluten-free diet (baseline) and on day 6 following a 3-day gluten challenge. Only significant p-values are shown. Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 10 of 20 Figure 6. Percentage of the activated gut-homing T cells of interest across the three T cell subsets analyzed: CD4 + , CD8 + , and TCRγδ + T cells. Only significant p-values are shown. 2.2. Characterization of CD4 + , CD8 + , and TCR γδ + T lymphocytes To improve the characterization of gut-homing T cells, we manually examined the variability in the expression of phenotypic markers within CD4 + , CD8 + , and TCRγδ + gluten-induced T cell populations. When at least 10 cells with this phenotype were detected, they were included in the analysis. The mean percentage of marker expression within each cell group is summarized in Figure 7. High expression levels of CXCR3, CCR9, CD49d, and CD39 were identified on day 6 in all three T cell populations. Some cells with the same markers as the studied CD8 + and TCRγδ + T cell populations were observed at baseline in patients with CD, but the percentage of CCR9 expression was significantly lower compared to that on day 6 in those cells. In the HC group, only one participant had at least 10 cells in each T cell subset, which was deemed sufficient for proper characterization. Elevated PD-1 expression was found only in CD4 + T cells. In contrast, CD8 + and TCRγδ + cells presented significantly higher expressions of HLA-DR compared to CD4 + cells. Intermediate values differing between the groups were observed in several markers related to the T cell nature. CD27 showed a higher expression in the CD4 + subset. CD45RA and CCR7 exhibited a low expression across the three T cell populations, and slight, but significant, differences were observed between them. Figure 7. Mean percentage and standard error of the mean of phenotypic marker expression in manually identified activated gut-homing CD4 + , CD8 + , and TCRγδ + T cells. Only significant p-values are shown, with p-values for differences between days displayed in blue and p-values for differences between cell types shown in black. . 3. Discussion In this study, we used spectral flow cytometry to examine peripheral blood cell populations in individuals with CD compared to HC, considering two conditions: at baseline Figure 6. Percentage of the activated gut-homing T cells of interest across the three T cell subsets analyzed: CD4+, CD8+, and TCRγδ+T cells. Only significant p-values are shown. Int. J. Mol. Sci. 2025,26, 2877 16 of 19 Appendix B Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 16 of 20 Figure A1. Density plots and marker expression gradients across the dimensionality reductions for (A) CD45 + and (B) CD3 + subsets. Appendix B Figure A2. Graphical representation of the four cell subpopulations differentially expressed between patients with celiac disease (CD) and healthy controls (HC) stratified by sex. (A) CD27 + Figure A2. Graphical representation of the four cell subpopulations differentially expressed between patients with celiac disease (CD) and healthy controls (HC) stratified by sex. (A) CD27 + CD45RA + HLA-DR + CD19 + CD20 + in relation to the total CD19 + CD20 + ; (B) CD49d+ β 7 hi CD38 + CD4 + in relation to the total CD4 + ; (C) CD103+ β 7 hi CD38 + CD8 + in relation to the total CD8 + ; and (D) CD103+β7hi CD38 + TCR γδ+ in relation to the total TCR γδ+ .P-values were calculated using an unpaired Student’s t-test. Only significant p-values are shown. Int. J. Mol. Sci. 2025,26, 2877 17 of 19 Int. J. Mol. Sci. 2025, 26, x FOR PEER REVIEW 17 of 20 CD45RA + HLA-DR + CD19 + CD20 + in relation to the total CD19 + CD20 + ; (B) CD49d+β7 hi CD38 + CD4 + in relation to the total CD4 + ; (C) CD103+β7 hi CD38 + CD8 + in relation to the total CD8 + ; and (D) CD103+β7 hi CD38 + TCRγδ + in relation to the total TCRγδ + . P-values were calculated using an unpaired Student’s t-test. Only significant p-values are shown. . Figure A3. Correlation between age and the frequency of each differentially expressed cell subtype in samples collected on day 6 following the 3-day gluten challenge in celiac disease (CD) and healthy controls (HC). (A) CD27 + CD45RA + HLA-DR + CD19 + CD20 + in relation to the total CD19 + CD20 + ; (B) CD49d+ β 7 hi CD38 + CD4 + in relation to the total CD4 + ; (C) CD103+ β 7 hi CD38 + CD8 + in relation to the total CD8+; and (D) CD103+β7hi CD38+TCRγδ+in relation to the total TCRγδ+. Int. J. Mol. Sci. 2025,26, 2877 18 of 19 Appendix C Table A1. Reagents used in the spectral flow cytometry protocol. Product Company Reference RBC lysis buffer (10X) Biolegend 420302 LIVE/DEAD™ Fixable Blue Stain Fluorescence Invitrogen L34961 True-Stain Monocyte Blocker™ Biolegend 426102 Brilliant Stain Buffer BD Biosciences 563794 anti-TCRγδ PerCP-eFluor 710 (clone: B1.1) Invitrogen 46-9959-42 anti-CX3CR1 in BV711 (clone: 2A9-1) Biolegend 341630 anti-CCR7 in BV421 (clone: G043H7) Biolegend 353208 anti-CCR9 in PE/Dazzle (clone: L053E8) Biolegend 358918 anti-CCR4 in BV750 (clone: 1G1) BD Biosciences 746980 anti-CD11c in e-Fluor®450 (clone: 3.9) Invitrogen 48-0116-42 anti-CD127 in APC-R700 (clone: HIL-7R-M21) BD Biosciences 565185 anti-CD3 in BV510 (clone: SK7) Biolegend 344828 anti-CXCR3 in PE/Cy7 (clone: G025H7) Biolegend 353720 anti-CD20 in Pacific Orange (clone: HI47) Invitrogen MHCD2030 anti-CD39 in BUV615 (clone: TU66) BD Biosciences 751269 anti-PD-1 in BV785 (clone: EH12.2H7) Biolegend 329930 anti-CD1c in AF647 (clone: L161) Biolegend 331510 anti-PD-L1 in BV650 (clone: 29E.2A3) Biolegend 329740 anti-integrin β7 in PerCP/Cy5.5 (clone: FIB27) Biolegend 121008 anti-CD38 in APC/Fire 810 (clone: HB-7) Biolegend 356644 anti-CD25 in PE/AF-700 (clone: 3G10) Invitrogen MHCD2524 anti-CD123 in SuperBright 436 (clone: 6H6) Invitrogen 62-1239-42 anti-CD14 in Spark Blue 550 (clone: 63D3) Biolegend 367148 anti-CD45 in PerCP (clone: 2D1) Biolegend 368506 Anti-CLA in BV605 (HECA-452) BD Biosciences 563960 anti-CD103 in PE (clone: Ber-ACT8) Biolegend 350206 anti-CD141 in BB515 (clone: 1A4) BD Biosciences 566017 anti-CD45RA in BUV395 (clone: 5H9) BD Biosciences 740315 anti-CD49d in BUV615 (clone: 9F10) BD Biosciences 751596 anti-CD19 in Spark NIR 685 (clone: HIB19) Biolegend 302270 anti-CD69 in PE/Cy5 (clone: FN50) Biolegend 310908 anti-HLA-DR in BUV563 (clone: G46-6) BD Biosciences 748340 anti-CD56 in BUV737 (clone: NCAM16.2) BD Biosciences 612766 anti-IgD in BUV480 (clone: IA6-2) BD Biosciences 566138 anti-CD16 in BUV496 (clone: 3G8) BD Biosciences 612944 anti-CD8 in BUV805 (clone: SK1) BD Biosciences 612889 anti-CD4 in cFLUOR®YG584 (clone: SK3) Cytek SKU R7-20041 anti-CD27 in APC/Cy7 (clone: M-T271) Biolegend 356424 References 1. 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