Cellular and plasma proteomic determinants of COVI... / 开普饭

Study design and clinical cohorts

First, we considered individuals who presented with respiratory illness symptoms and had a physician-ordered SARS-CoV-2 test performed at the Barnes Jewish Hospital between 26 March 2020 and 28 August 2020 (Washington University 350 (WU350) cohort). Based on nasopharyngeal testing by PCR with reverse transcription (RT–PCR), participants were defined as SARS-CoV-2 positive (CV; 140 females and 173 males) or SARS-Cov-2 negative (NCV; 98 females and 40 males; Fig. 1a). The population was heterogeneous for body mass index (BMI), where nearly half of individuals were moderately or severely obese (BMI > 33; Extended Data Fig. 1a). Given that obesity is a recognized risk factor for severe COVID-19 (ref. ¹⁶) and known to strongly impact immune and proteomic homeostasis²³, we chose to minimize these confounding factors in our analysis and excluded participants with moderate and severe obesity. Our selected CV and NCV cohorts consisted of 80 individuals, with age and sex distributions proportional to those of the nonobese individuals (53 CV individuals: median BMI, 25.5; interquartile range (IQR), 21.9–28.4; 27 NCV individuals: median BMI, 27.3; IQR, 25.6–29.8; Extended Data Fig. 1b,c). We cannot conclusively rule out SARS-CoV-2 infection in participants with negative SARS-CoV-2 tests because the false-negative rate of the nasopharyngeal RT–PCR test is reported to be 0.018–0.33 (ref. ²⁴); however, 13 of 27 NCV individuals were retested, and none of the retests was positive for SARS-CoV-2, and none of the 27 individuals had a subsequent hospital readmission. The most common diagnoses at discharge were pneumonia and chronic obstructive pulmonary disease (Supplementary Table 1). The majority of nonobese individuals with COVID-19 were males (~70%), and the average age was 71 years. The age of individuals without COVID-19 was distributed more broadly, with an average age of 55 years old (Fig. 1a and Extended Data Fig. 1c). We divided the participants with COVID-19 into three subgroups based on admission to an intensive care unit (ICU) and survival criteria: (1) CV_moderate, including individuals who were not admitted to the ICU during treatment, (2) CV_severe, including individuals who were admitted to the ICU, and (3) CV_deceased, including individuals who did not survive the illness (Supplementary Tables 2 and 3 and Fig. 1a). Most individuals admitted to the ICU were assigned a severity score based on a time-weighted average of discharge readiness²⁵. Of note, our ICU-based definition of severity correlated well with known inflammation characteristics such as C-reactive protein levels (Extended Data Fig. 1e,f) and other common parameters of disease severity such as intubation and severity score (Extended Data Fig. 1d). Consistent with the known increase in COVID-19 severity with age, the average age of the deceased cohort was higher compared to individuals with moderate or severe COVID-19 (Supplementary Table 2 and Fig. 1a).

Fig. 1: Study outline and clinical characterization of healthy and COVID-19/non-COVID-19 cohorts.

Age is a known susceptibility factor for COVID-19, and it also significantly affects the immune and proteomic homeostasis in healthy individuals^20,26. Therefore, to discriminate the effect of aging from disease-associated changes, we expanded our study to include a cohort of 148 healthy nonobese individuals aged 25 to 80 years, divided into five age groups (ABF300 cohort; Fig. 1a and Extended Data Fig. 1d). These blood samples were collected before the COVID-19 pandemic as part of an ongoing study of healthy human aging. In total, we analyzed 219 samples using clinical blood tests, complete blood count differentials, mass cytometry immunostaining (CyTOF) and plasma proteomics. Joint analysis of the healthy ABF300 cohort and the WU350 COVID-19 and non-COVID-19 cohorts revealed unique age-specific and disease-specific features of immune and physiological responses to COVID-19.

Clinical laboratory characteristics

Complete blood count differential analysis showed a statistically significant increase in the absolute numbers of white blood cells (WBCs) in NCV and non-moderate CV groups (Fig. 1b; see Extended Data Fig. 1f for statistical evaluation between all groups). This increase was attributed to a statistically significant increase in numbers of neutrophils (adjusted P value (P_adj.) < 0.001), while changes in lymphocyte and monocytes numbers did not reach statistical significance when comparing NCV and CV groups to the age-matched healthy control groups (Fig. 1b and Extended Data Fig. 1g). This observation is consistent with previous reports^27,28, including the increase in immature granulocytes with disease severity^14,29 (Fig. 1b).

We observed that red blood cell (RBC) count decreased within the oldest age group (A versus E; P_adj. < 0.001) and that RBC count in NCV participants and individuals with moderate COVID-19 did not statistically differ from corresponding age-matched values, while individuals with severe COVID-19 had a statistically lower RBC count compared to healthy individuals of any age (Fig. 1c; see Extended Data Fig. 1g for statistical evaluation between all groups). Similar alterations were observed for hemoglobin levels (Fig. 1c and Extended Data Fig. 1g). Strikingly, RBC distribution width was distinctly associated with COVID-19 at all severity levels relative to both healthy people and individuals without COVID-19 (Fig. 1c), consistent with previous works^30,31. Lastly, platelet counts demonstrated a decreasing trend that appeared specific to individuals with COVID-19, although it did not reach significance in our cohorts (Extended Data Fig. 1g).

Several biochemical parameters changed dramatically in an inflammation and/or COVID-19-specific manner. Albumin concentration, indicative of liver health, did not decrease with age, but it significantly decreased during inflammation, particularly in COVID-19 groups of all severity levels (Fig. 1d; see Extended Data Fig. 1h for statistical evaluation between all groups). Calcium significantly decreased in individuals with COVID-19 compared to all ages of healthy controls and individuals without COVID-19, consistent with previous reports³², yet our data show that individuals without COVID-19 demonstrated only a nonsignificant decreasing trend compared to healthy individuals (Fig. 1d). Of note, unlike other blood ions (potassium, sodium and chloride), calcium levels did not increase with age (Extended Data Fig. 2a,b). Biochemical measures indicative of kidney function showed patterns that were strikingly specific to individuals with COVID-19 and correlated with disease severity. Specifically, creatinine and urea nitrogen levels did not differ between healthy individuals and participants without COVID-19, while they increased progressively in individuals with COVID-19, with the highest levels reached in the deceased cohort (Fig. 1d). Notably, urea nitrogen levels, but not creatinine levels, were age dependent—increasing with age within the healthy range (Extended Data Fig. 2c,d). However, the significant urea nitrogen level increase in severe and deceased COVID-19 groups was not attributed to age, as the COVID-19-dependent increase was significant even when compared to the oldest age group (P_adj. < 0.05, CV_severe versus cohort E; P_adj. < 0.001, CV_deceased versus cohort E; Extended Data Fig. 1i). Other age-dependent biochemical properties observed in the healthy control cohort included C-peptide levels³³, lactic acid dehydrogenase levels³⁴, glucose³⁵, thyrotropin³⁶ and DHEA³⁷ (Extended Data Fig. 2).

CyTOF analysis of peripheral blood mononuclear cells

To understand changes in immune cell populations with the disease, we performed mass cytometry (CyTOF) on PBMCs of 219 blood samples from the healthy and disease cohorts using 28 myeloid and lymphoid markers (Methods). A subset of target proteins was selected based on single-cell RNA sequencing (scRNA-seq) of PBMCs to maximize cellular subset resolution. Specifically, we included mucosal-associated invariant T (MAIT) cell and γδ T cell markers (TCRVA7.2 and TCRγδ, respectively) and antibodies to granzymes GZMK and GZMB because we³⁸ and others³⁹ have shown that these proteins discriminate two major effector memory T (T_EM) CD8⁺ cell subpopulations. We identified the major cell populations such as T cells (CD4⁺ T cells, CD8⁺ T cells, γδ T cells and MAIT cells), B cells, natural killer (NK) cells and myeloid cells (Fig. 2a) using unsupervised clustering and distribution of key lineage markers (Extended Data Fig. 3b and Methods).

Fig. 2: Defining major immune subsets in PBMCs by CyTOF for healthy and COVID-19/non-COVID-19 groups.

Differences between the major cell subpopulations can be appreciated directly from the distributions seen in cell density plots (Extended Data Fig. 3c). B cell proportions significantly increased in both SARS-CoV-2-positive and SARS-CoV-2-negative disease groups in line with previously reported results¹³ (Fig. 2b; see Extended Data Fig. 3d for statistical evaluation between all groups), indicating that this increase is a general characteristic of the immune response to pulmonary disease. Proportions of CD4⁺ T cells for NCV, CV_moderate and CV_deceased groups were decreased relative to age-matched healthy controls. A similar decrease in CD4⁺ T cell proportions during SARS-CoV-2 and influenza infection was recently reported^40,41 (Fig. 2b and Extended Data Fig. 3d). Proportions of CD8⁺ T cells were increased in the group with moderate COVID-19 compared to the age-matched healthy group (group E), while there was no statistically significant difference for severe and deceased individuals relative to healthy individuals of any age. Of note, within the healthy cohort, CD8⁺ T cells proportions were significantly decreased in the oldest donors (group E; >65 years old) relative to younger groups. Next, we analyzed major immune cell populations individually (Fig. 2b).

CD4⁺ cells

We performed dimensionality reduction and clustering based on the relevant subset of markers (Methods) and identified 12 CD4⁺ T cell subpopulations (Fig. 2c,d). They included three subsets of CD4⁺ T_EM cells (that is, CCR7⁻CD45RO⁺) divided based on EOMES and TBET expression, two subpopulations of central memory T (T_CM) CD4⁺ cells (that is, CCR7⁺CD45RO⁺) distinguished by the level of CD45RO expression (medium or low), two subpopulations of regulatory T (T_reg) CD25⁺ CD4⁺ cells (CD45RA positive and CD45RO positive), three subpopulations of naïve CD4⁺ T cells based on the combinatorial expression of CD25 and SELL (CD62L) and two subpopulations with generally low levels of both CD45RA and CD45RO surface markers, which we denoted as RA^lowRO⁻ (Fig. 2c,d and Extended Data Fig. 4a). Changes in population structure associated with age and disease were evident from the density plots of individual groups (Fig. 2f). Multidimensional scaling (MDS), computed based on the cluster percentages, also demonstrated distinct age-dependent and disease-dependent sample separation (Fig. 2g).

A decrease in naïve CD4⁺ T cells was one of the most prominent age-associated features, and this population was further diminished in individuals with pulmonary disease, both in SARS-CoV-2-positive and SARS-CoV-2-negative groups (Fig. 2h; see Extended Data Fig. 4b for statistical evaluation between all groups). Interestingly, the population of naïve CD4⁺ T cells lacking SELL surface expression was distinctly upregulated (see naïve SELL⁻ population in Fig. 2h; Extended Data Fig. 4b) in disease cohorts, likely comprising a transient population associated with an active immune response. A similar pattern was observed for a subset of T_CM cells characterized by low levels of CD45RO expression (CM RO^low), which increased specifically in the disease conditions. Among the three subsets of T_EM cells, the subpopulation lacking both TBET and EOMES (TBET⁻EOMES⁻CD4⁺) expression significantly increased in disease groups, likely indicating effector cells associated with the immune response. Proportions of CD4⁺ T_EM cells that expressed both TBET and EOMES were specifically increased in moderate but not severe or deceased COVID-19-infection cohorts. This subpopulation of CD4⁺ T cell expresses cytotoxicity markers (GZMB and GZMK), which might be beneficial in disease progression. This population also appeared to accumulate with age, albeit the difference did not reach statistical significance (Fig. 3f and Extended Data Fig. 4b). This population likely corresponds to recently reported cytotoxic CD4⁺ T cells that dramatically increase in supercentenerians⁴².

Fig. 3: CD8⁺ T cells in COVID-19/non-COVID-19 groups lose the conventional effector memory phenotype, with a COVID-19-specific increase in HLA-DR⁺CD38⁺ CD8⁺ T cells.

Additionally, we identified a distinct CD4⁺ T cell subpopulation, RA^lowRO⁻CD25^low, which progressively accumulated with age (Fig. 3f and Extended Data Fig. 4b). To our knowledge, this is the first time this cell population has been defined as age dependent. Interestingly, this population was increased in individuals with severe COVID-19 but not in those with moderate or no COVID-19, compared to younger controls (that is, group A or B; Extended Data Fig. 4b).

Taken together, the CD4⁺ T cell compartment demonstrates age-associated (increase in RA^lowRO⁻CD25^low, loss of naïve cells, increasing trend of TBET⁺EOMES⁺ and central memory populations) and inflammation-associated remodeling, where its key features (further loss of conventional naïve cells, increase in TBET⁻EOMES⁻, CD45RO^low and naïve SELL⁻ cells) appear to be associated with the respiratory pathology immune response rather than COVID-19-specific responses, with the possible exception of the T_EM TBET⁺EOMES⁺ subpopulation which increases strongly in individuals with moderate COVID-19.

CD8⁺ cells

CD8⁺ T cells demonstrated the most striking remodeling in healthy aging and inflammatory contexts (Fig. 3). In total, we identified ten CD8⁺ T cell clusters (Fig. 3a–c and Extended Data Fig. 5a). In addition to naïve and CD8⁺ T_CM cells, we defined eight distinct subpopulations of the CD8⁺ T_EM cells—five subpopulations in healthy individuals and three subpopulations that arise during disease conditions (Fig. 3d–f and Extended Data Fig. 5b). MDS plots and density plots demonstrated distinct CD8⁺ compartment remodeling associated with aging and disease (Fig. 3d,e). Consistent with the published scRNA-seq data³⁹ and our previous observations³⁸, CD8⁺ T_EM cells can be divided into two major populations based on expression of GZMK and GZMB (Fig. 3c). In healthy individuals, GZMB-expressing CD8⁺ T_EM cells were mostly CD45RA positive, identifying them as T_EMRA, and were divided into CD27⁺ (4.1% ± 3.7% of total CD8⁺ T cells) and CD27⁻ (9.4% ± 10.8% of total CD8⁺ T cells) subpopulations (Fig. 3b,c). We recently demonstrated that proportions of GZMK⁺CD8⁺ T cells among the total CD8⁺ T cells increase during healthy aging³⁸. However, surface markers distinguishing this population remained unclear. Here, we find that GZMK⁺CD8⁺ T_EM cells can be identified by the surface expression of CCR5 and are predominantly CD57 negative (Fig. 3b,c and Extended Data Fig. 5d). These data further extend our previous observation to highlight the gradual age-dependent increase in GZMK⁺CD8⁺ T_EM cells. Additionally, healthy aging was accompanied by a substantial decrease in naïve cells, a significant progressive increase in T_CM cells and an increasing trend of T_EMRA cells, although the latter did not reach statistical significance (Fig. 3f; see Extended Data Fig. 5b for statistical evaluation between all groups). This observation extends our previous work, in which the proportion of GZMK⁺CD8⁺ T cells among the total CD8⁺ T cell population was shown to increase with age based on a comparison of young and old populations³⁸. In addition to these age-dependent cell populations, two distinct PD-1-positive subsets were present in the healthy individuals, each at ~5% of total CD8⁺ T cells: GZMB⁺GZMK⁻ and GZMB⁺GZMK⁺ T_EM cells (Fig. 3f). These cell subpopulations were characterized by a PD1⁺CD57⁺CD45RA⁻ phenotype, yet they differed in the expression of CD27 (Fig. 3c). These cell subpopulations were present at steady levels across the aging subgroups (Fig. 3f).

The disease-associated inflammatory response was accompanied by a pronounced remodeling of the CD8⁺ T cell compartment. Three major cell populations emerged in disease groups (Fig. 3f). The largest increase was observed for inflammatory GZMB⁺GZMK⁻ and GZMB⁺GZMK⁺ T cells that differed from the corresponding healthy counterparts (T_EMRA and T_EM GZMK⁺ T cells, respectively) in that they lost CD45RA and CD27 surface expression (Fig. 3b,c). Lack of surface expression of CD45RA, CD27, CD28 and PD-1 proteins indicated that these could be effector cells⁴³. Appearance of these cell populations was a shared feature of all individuals independent of COVID-19 status. However, an additional inflammatory cell population characterized by expression of HLA-DR, CD38 and PD-1 was found almost exclusively in individuals with COVID-19. The appearance of this cell population was recently reported^13,44, but specificity to the COVID-19 immune response versus non-COVID-19 respiratory pathology immune response has not yet been established. The increase in these three inflammation-specific cell populations was paralleled by a decrease in the conventional steady-state subpopulations: T_EMRA subpopulations and GZMK-expressing T_EM subpopulations decreased to very low levels in all inflammatory groups (Fig. 3f). Interestingly, unlike in CD4⁺ T cells, naïve CD8⁺ T cells did not significantly decrease compared to corresponding age-matched controls (CV/NCV groups compared with E cohort; Fig. 3f and Extended Data Fig. 4b). This result suggests that, in this context, effector CD8⁺ T cells may arise from T_EM subpopulations, for example, GZMK⁺ T_EM cells acquiring the GZMK⁺GZMB⁺ inflammatory T (T_INFLAM) phenotype and GZMB⁺ T_EMRA cells acquiring the GZMK⁻GZMB⁺ T_INFLAM phenotype.

Taken together, we find that peripheral blood CD8⁺ T cells undergo major remodeling during both healthy aging and inflammatory contexts. During aging, there is a loss of naïve cells and an increase of T_CM and GZMK⁺ T_EM cells. Inflammatory remodeling is characterized by a decrease in conventional T_EM subpopulations and an increase in inflammatory effector-like subpopulations and HLA-DR⁺CD38⁺PD-1⁺ CD8⁺ T cells, which are specific to individuals with COVID-19.

NK cells, B cells and myeloid cells

NK cells were split into 11 subpopulations based on the expression of CD16, CD57, CD56, GZMK and SELL (Fig. 4a–c and Extended Data Fig. 6a). There was major inflammatory-associated remodeling of NK cells (Fig. 4d,e), as seven clusters demonstrated a difference between the healthy group and at least one inflamed group: CD56⁺CD57⁻GZMK⁺ (enriched in CV_moderate group), CD56⁻CD57⁻CD16⁻ (enriched in disease groups except for CV_severe), CD56^dimCD57⁺CD16⁻ (enriched in NCV group) and CD56^dimCD57^low (enriched in NCV and CV_moderate groups; Fig. 4f and Extended Data Fig. 6b). Two clusters did not change with age but significantly decreased across all disease cohorts: CD56⁺CD57^low and CD56⁺CD57⁺ (Fig. 4f; see Extended Data Fig. 6b for statistical evaluation between all groups). The CD56⁺CD57⁺SELL⁺ cluster showed a similar decreasing pattern but did not reach statistical significance. Only one cluster changed significantly with age: the CD56⁺CD57⁻SELL⁺ cluster decreased with age (cohort E was significantly lower than cohort A; Extended Data Fig. 6b), yet it did not change with inflammation. This observation is consistent with previous reports of a decrease in CD56⁺ NK cells with age⁴⁵ (Extended Data Fig. 6c).

Fig. 4: Inflammatory remodeling of NK and B cells.

Our panel included a limited number of markers to resolve B cell subpopulations. B cells separated into six clusters (Fig. 4g–i) with no significant change detected in these subpopulations across age subgroups (Extended Data Fig. 6e), and there was no clear separation between samples in the MDS plot (Fig. 4k). However, the density plots indicated some inflammation-associated remodeling (Fig. 4j). Specifically, consistent with previous reports¹³, we observed an increase in CD27⁺CD38⁺ plasmablasts in individuals with severe COVID-19 (in comparison with age-matched healthy E cohort; Fig. 4l; see Extended Data Fig. 6e for statistical evaluation between all groups). This cell subpopulation is specific to individuals with severe COVID-19 and was not significantly different between healthy individuals and those without COVID-19. The B cell memory population, defined as CD27⁺CD38⁻SELL⁺, demonstrated a COVID-19-specific decrease in proportions among the B cells (statistically significant for individuals with severe COVID-19 versus participants in all age groups).

Myeloid cells demonstrated remodeling associated with infection (Extended Data Fig. 7a–d): proportions of classical monocytes and dendritic cells significantly decreased while proportions of HLA-DR^low monocytes significantly increased in the disease cohorts relative to healthy controls (Extended Data Fig. 7e,f). This DR^low subset was previously associated with an immunosuppressive monocyte phenotype⁴⁶, consistent with the general features of immunosuppression reported for COVID-19 recently⁴⁷.

Protein signatures of disease linked to healthy aging

Next, we used the SomaScan assay to analyze the proteomic signature from CV and NCV groups (WU350) and the healthy aging cohort (ABF300). SomaScan quantifies ~4,700 proteins in relative units of intensity, allowing data comparison within homogeneously collected and processed samples (Supplementary Tables 4 and 5). One caveat of our study was that samples for the cohorts were collected using different collection approaches: WU350 samples were collected in EDTA tubes, and ABF300 samples were collected in heparin tubes. While this did not affect the measurement of cellular proportions, proteomic data from the cohorts was required to be analyzed first within each cohort and then individual aging/disease signatures could be compared across cohorts.

The comparison of CV and NCV groups identified 435 upregulated proteins in individuals with COVID-19 and 464 upregulated proteins in individuals without COVID-19 (Fig. 5a). Most of these differences were driven by the severe and lethal cases of COVID-19 (Fig. 5b). Overall, the up/down COVID-19-specific signatures demonstrated a progressive increase/decrease with disease severity (Fig. 5c,d). The same pattern emerged when each COVID-19 cohort was compared to individuals without COVID-19 (Extended Data Fig. 8a–c). A relatively small number of proteins were differentially expressed between the NCV and CV_moderate disease groups (20 CV-specific and 7 NCV-specific upregulated proteins; Fig. 5b and Extended Data Fig. 8d). Proteins upregulated in the CV group (Fig. 5c) included complement protein C9; interferon response markers MX1, ISG15 and IFIT3; ferritin subunits FTL and FTH1; heparin-binding growth factors pleiotrophin (PTN) and midkine (MDK); growth factors CLEC11A, HAMP, TINAGL1 and SFRP1; inflammation-associated soluble factors serum amyloid a1 (SAA1), fibrinogen like protein (FGL1) and granulin (GRN); soluble forms of surface receptors FOLR2 and members of CD85 family (LILRB2 and LILRA3); and two additional proteins CHST12 and DKK3 (Fig. 5d). Notably, FGL1 and LILRA3 have the potential to directly negatively impact CD8⁺ T cell activity by engaging with LAG3 or interfering with human leukocyte antigen (HLA) class I/II accessibility^48,49. The proteins upregulated in NCV groups (Fig. 5e,f) compared to individuals with COVID-19 included AHSG (fetuin-A), KLRC4, CLEC3B, afamin (AFM) and others.

Fig. 5: SomaLogic plasma protein profiling demonstrates age-specific and inflammation-specific signatures in individuals with COVID-19.

Given the different distribution of ages between the pulmonary disease cohorts, we next examined the degree to which age-related proteomic changes shape this behavior. Comparison of young (A) versus old (E) subgroups of the aging cohort revealed 241 proteins that were statistically upregulated with age and 140 downregulated proteins (Fig. 5g). Our data are consistent with the results previously published from our group and others^{26,50,51,52,53,54}: proteins most upregulated with age were GDF15, SOST and ADAMTS5, as well as PTN, TAGLN, TREM2, WISP2, MYL3 and MLN, while most downregulated proteins included RET, SELL and KIT, as well as MSMP, CILP2, CTSV and CR2 (Extended Data Fig. 8e). Because we also characterized our cohorts using clinical blood tests, we compared proteomics data with the blood biochemistry analyses obtained for the same individuals from the healthy aging cohort (Fig. 1a and Supplementary Tables 6–10). A number of measured proteins strongly correlated with the clinical blood test results (Extended Data Fig. 8f): (1) creatinine kinase strongly correlated with plasma levels of SLC26A7, CKB, ACTN2, TNNI2 and MYBPC1; (2) clinical alanine aminotransferase levels correlated with plasma levels of UGDH, ALDH1A1, ASL, ALDOB, PSAT1, ACY1, FBP1 and DCXR1; (3) C-peptide and insulin levels strongly anti-correlated with IGFBP1 and ADIPOQ (as expected, insulin levels measured by clinical blood test strongly correlated with insulin levels analyzed via SomaScan profiling); (4) clinical measurements of direct high-density lipoprotein cholesterol levels positively correlated with EHMT2 protein levels and anti-correlated with WNT5A protein levels, while the latter (5) also correlated with general triglyceride levels; (6) clinical osteocalcin levels were strongly correlated with plasma levels of CHAD protein; (7) clinical thyrotropin hormone levels strongly correlated with the corresponding protein (CGA/TSHB) levels in the proteomic data; (8) and lastly, clinically measured unsaturated iron binding capacity was strongly correlated with FTL/FTH1 and NEO1 protein levels. While this high level of concordance does not imply that SomaScan-based profiling can substitute for clinical measurements, it demonstrates the capability of unbiased profiling in characterizing the physiological state.

Gene-set enrichment analysis (GSEA) analysis demonstrated that the COVID-19 versus non-COVID-19 differentially expressed proteins strongly associated with the up/down aging signatures, consistent with the differences in the age distribution of those cohorts (Fig. 5h,i). Furthermore, we found that the COVID-19 versus non-COVID-19 signatures significantly overlapped with the up/down aging signatures (Fig. 5j,k) but not vice versa (Extended Data Fig. 8g,h), underscoring the importance of taking age into account when considering determinants of COVID-19. We found 337 unique proteins that were upregulated in COVID-19 and 421 proteins that were downregulated in individuals with COVID-19 compared to those without COVID-19 that were not age dependent. Age-associated proteins that were also significantly different in the COVID-19 versus non-COVID-19 comparison included PTN, SFRP1 and DKK3, which increased with age, and CLEC3B, which decreased with age. It is interesting to note the dissimilar age-associated behavior of two heparin-binding proteins (MDK and PTN) that were both upregulated in the COVID-19 group relative to the non-COVID-19 group (Fig. 5l). Consistent with our data, PTN was previously associated with aging⁵², while MDK does not change with age, yet serum concentrations of MDK are linked to heart injury conditions⁵⁵. Another protein associated with age and COVID-19, SFRP1, a soluble mediator of WNT signaling, has also been linked to modulation of cardiac function⁵⁶. Another WNT signaling modulator, DKK3, was previously linked to aging and is considered a major indicator of muscle atrophy⁵⁷. A small number of proteins behaved in the opposite manner between aging and COVID-19 (11 upregulated with CV and downregulated with age, and 7 vice versa), which included inflammatory mediators (CCL21 and SEMA4A) or apolipoproteins (APOA4 and APOE2; Extended Data Fig. 8g,h).

COVID-19 protein profile linked to hepatocytes and muscle secretomes

To understand the broad-level differences between individuals with COVID-19 and individuals without COVID-19, we performed pathway enrichment analysis on the differential proteins. Several pathways were upreguated or downregulated in a disease-specific manner (Fig. 6a). The pathways most upregulated in individuals with COVID-19 were associated with extracellular matrix proteins (for example, WISP2 and FBLN5) and were also profoundly associated with age (Fig. 6b,c). Similarly, soluble forms of TREM2 and IGFBP2 were increased in individuals with COVID-19 and older healthy individuals. Several COVID-19-specific pathways were independent of aging signatures and included inflammatory processes (interferon, IL-6 and IL-2/stat5), complement pathways and glycosaminoglycan metabolism (Fig. 6d,e). Conversely, proteins from MAP kinase-associated pathways were downregulated in the plasma of individuals with COVID-19 relative to that of individuals without COVID-19. These proteins were mostly independent of age and included MAP2K3, BRAF, HRAS and MAP2K4. (Fig. 6f,g).

Fig. 6: Pathway enrichment analysis distinguishes COVID-19 from non-COVID-19 inflammation.

We next evaluated if cell-type-specific signatures of PBMC subpopulations are enriched in the COVID-19-specific proteome. None of the individual cell types were enriched; however, a myeloid signature (monocytes and neutrophils) was indeed upregulated in individuals with COVID-19 (Extended Data Fig. 8). To further investigate cell-type specificities, we extracted tissue-specific transcriptional signatures from the Genotype-Tissue Expression (GTEx) database (Fig. 7a; see Methods for details and Supplementary Table 11 for list of genes) and evaluated these signatures against the proteomic data ranked by the comparisons of CV versus NCV groups or by aging comparison (A versus E cohorts; Fig. 7b). Individuals with COVID-19 had a pronounced increase in liver-specific proteins accompanied by a significant decrease of muscle-specific proteins. These tissue-associated changes were unique to the COVID-19 cohort and did not vary with age. Instead, artery/aorta-specific proteins were highly upregulated with age (Fig. 7b).

Fig. 7: COVID-19 plasma protein signatures are linked to hepatocytes and skeletal muscle secretomes.

Given the distinct enrichment of these tissues, we mined public scRNA-seq data for the liver⁵⁸ and aorta⁵⁹ to understand if any specific cell type is driving these signatures. When projecting 54 liver-specific genes enriched in the comparison of CV and NCV groups, we observed a very strong specificity to hepatocytes (Fig. 7c–e), indicating an important role in regulating plasma protein level alterations in COVID-19 infection. The artery/aorta-specific signature enriched in aging also demonstrated cell-type-specific enrichment in smooth muscle cells (Fig. 7f–h).

Cellular and plasma proteomic determinants of COVI...