Identification of an immunological signature of long COVID syndrome

Abstract

Introduction: Acute COVID-19 infection causes significant alterations in the innate and adaptive immune systems. While most individuals recover naturally, some develop long COVID (LC) syndrome, marked by persistent or new symptoms weeks to months after SARS-CoV-2 infection. Despite its prevalence, there are no clinical tests to distinguish LC patients from those fully recovered. Understanding the immunological basis of LC is essential for improving diagnostic and treatment approaches.

Methods: We performed deep immunophenotyping and functional assays to examine the immunological profiles of LC patients, individuals with active COVID-19, recovered patients, and healthy donors. This analysis assessed both innate and adaptive immune features, identifying potential biomarkers for LC syndrome. A Binomial Generalized Linear Model (BGLM) was used to pinpoint immune features characterizing LC.

Results: COVID-19 patients exhibited depletion of innate immune cell subsets, including plasmacytoid and conventional dendritic cells, classical, non-classical, and intermediate monocytes, and monocyte-derived inflammatory dendritic cells. Elevated basal inflammation was observed in COVID-19 patients compared to LC patients, whose immune profiles were closer to those of healthy donors and recovered individuals. However, LC patients displayed persistent immune alterations, including reduced T cell subsets (CD4, CD8, Tregs) and switched memory B cells, similar to COVID-19 patients. Through BGLM, a unique adaptive immune signature for LC was identified, featuring memory CD8 and gd T cells with low proliferative capacity and diminished expression of activation and homing receptors.

Discussion: The findings highlight a unique immunological signature associated with LC syndrome, characterized by persistent adaptive immune dysregulation. While LC patients displayed recovery in innate immune profiles comparable to healthy and Recovered individuals, deficits in T cell and memory B cell populations were evident, differentiating LC from full recovery. These findings provide insights into LC pathogenesis and may support the development of diagnostic tools and targeted therapies.

Keywords: Long COVID; SARS-CoV-2 infection; immune dysregulation; immune response; immunological signature; post-acute sequelae of COVID19.

Figure 1

Study design. Cohort of enrolled COVID (N=50) and LC (N=10) patients and HD (N=38) and Recovered (N=31) subjects. Myeloid and lymphoid cell populations, cytokine/chemokine production and activation markers were analyzed by high-parameter flow cytometry and by bioinformatic modeling.

Figure 2

Myeloid cell population counts and distribution in HD, COVID, LC and Recovered groups. (A) Graph showing the percentage of DC (pDC, cDC1, cDC2), monocytes (NcMono1, NcMono2, cMono and IntMono) and MSDC cell populations within HLA-DR+ myeloid cells in HD, COVID, LC and Recovered groups based on gating strategy shown in Supplementary Figure 1A . (B) Principal Component Analysis (PCA) of the myeloid cell subpopulation counts matrix derived from FC data is shown. Each dot represents one variable in HD, COVID, LC and Recovered groups. (C) XGBoost model was fitted on each training set and their performances were evaluated on the respective test sets using the auROC metrics. ROC curves of COV vs Rest (including LC, HD, Recovered) auROC value 0.92. (D–G) Graph showing the counts obtained by FC data of NcMono1, cDC1, pDC and MDSC cell populations in HD, COVID, LC and Recovered groups. Box and whiskers represent median of values with interquartile range. COVID (N=50), LC (N=10), HD (N=38), Recovered (N=31). Wilcoxon Rank Sum test for independent groups with the Holm p-value correction is shown. *** p<0.001 **** p<0.0001. No symbol, not significant.

Figure 3

Ex vivo production of cytokines and chemokines in myeloid cell populations in HD, LC, COVID and Recovered groups. (A) Principal Component Analysis (PCA) and (B) heatmap illustrating the standardized values of chemokine- and cytokine-producing myeloid absolute cell counts. For PCA each dot represents one variable. (C) XGBoost model was fitted on each training set and performances were evaluated on the respective test sets using the auROC metrics. ROC curves of HD vs Rest (including LC, COVID, Recovered) auROC value 0.90, COV vs Rest (including LC, HD, Recovered) (upper) auROC value 0.94, LC vs Rest (including HD, COVID, Recovered) auROC value 1 and Recovered vs Rest (including LC, COVID, HD) (bottom) auROC value 0.83. (D–F) Graph showing the counts obtained by FC data of NcMono1/MCP1, NcMono2/MCP1 and NcMono1/IL12 in HD, COVID, LC and Recovered groups. Box and whiskers represent median of values with interquartile range. COVID (N=50), LC (N=10), HD (N=38), Recovered (N=31). Wilcoxon Rank Sum test for independent groups with the Holm p-value correction is shown. * p<0.05, ** p<0.01, **** p<0.0001. No symbol, not significant.

Figure 4

Ex vivo analysis of cytokine/chemokine production on myeloid cell populations upon LPS and R848 stimuli. (A, B) Heatmap illustrating the standardized values of chemokine- and cytokine-producing myeloid cell counts obtained by FC data upon LPS (A) and R848 (B) stimulations. (C) Principal Component Analysis (PCA) illustrating the standardized values of chemokine- and cytokine-producing myeloid cell counts obtained by FC data upon LPS and R848 stimulation. Each dot represents one variable. (D) XGBoost model was fitted on each training set and evaluated their performances on the respective test sets using the auROC metrics. ROC curves of HD vs Rest (including LC, COVID, Recovered) auROC value 0.85, COV vs Rest (including LC, HD, Recovered) (upper) auROC value 0.96, LC vs Rest (including HD, COVID, Recovered) auROC value 0.94 and Recovered vs Rest (including LC, COVID, HD) (bottom) auROC value 0.85. (E–G) Graph showing the absolute cell counts of NcMono1/IL6 (R848), NcMono1/MCP1 (LPS), NcMono1/IL1β (LPS) in HD, COVID, LC and Recovered groups. Box and whiskers represent median of values with interquartile range. COVID (N=50), LC (N=10), HD (N=38), Recovered (N=31). Wilcoxon Rank Sum test for independent groups with the Holm p-value correction is shown. * p<0.05, **p<0.01, *** p<0.001 **** p<0.0001. No symbol, not significant.

Figure 5

Lymphoid cell population analysis in HD, LC, COVID and Recovered groups. (A) Principal Component Analysis (PCA) illustrating the standardized values of lymphocyte immune cell population counts obtained by FC data. Each dot represents one variable. (B) XGBoost model was fitted on each training set and evaluated their performances on the respective test sets using the auROC metrics. ROC curves of HD vs Rest (including LC, COVID, Recovered) auROC value 0.82, COV vs Rest (including LC, HD, Recovered) (upper) auROC value 0.94, LC vs Rest (including HD, COVID, Recovered) auROC value 1 and Recovered vs Rest (including LC, COVID, HD) (bottom) auROC value 0.82. (C) Graph showing the top (N=10) variables importance of lymphocyte immune cell population counts obtained by FC data as relative gain importance. (D–F) Graph showing the counts of Bcells/ASC, mUSW/CD25, mSW/CXCR5 in HD, COVID, LC and Recovered groups. Box and whiskers represent median of values with interquartile range. COVID (N=50), LC (N=10), HD (N=38), Recovered (N=31). Wilcoxon Rank Sum test for independent groups with the Holm p-value correction is shown. * p<0.05, **p<0.01, *** p<0.001 **** p<0.0001. No symbol, not significant.

Figure 6

Classification between LC patients and HD/Recovered subjects by local optimum. A GLM model was fitted to find the local optimum and classify LC patients versus HD/Recovered subjects. ROC curves (left) and counts obtained by FC data (right) of (A) mCD8/Ki67 (B) gd&DN/CXCR5, (C) NcMono2/TNFα, (D) mCD8/CXCR5, (E) gd&DN/CCR6, (F) gd&DN/Ki67 shown. Graphs (left) show ROC curves and AUC values referring to the top variables. Graphs (right) show all values as count obtained by FC data in HD and Recovered (as group of comparison) and LC group. Box and whiskers represent median of values with interquartile range. Wilcoxon Rank Sum test for independent groups with the Holm p-value correction is shown. **p<0.01, *** p<0.001 **** p<0.0001. No symbol, not significant.