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Microvascular and proteomic signatures overlap in COVID-19 and bacterial sepsis: the MICROCODE study



Although coronavirus disease 2019 (COVID-19) and bacterial sepsis are distinct conditions, both are known to trigger endothelial dysfunction with corresponding microcirculatory impairment. The purpose of this study was to compare microvascular injury patterns and proteomic signatures in COVID-19 and bacterial sepsis patients.

Methods and results

This multi-center, observational study included 22 hospitalized adult COVID-19 patients, 43 hospitalized bacterial sepsis patients, and 10 healthy controls from 4 hospitals. Microcirculation and glycocalyx dimensions were quantified via intravital sublingual microscopy. Plasma proteins were measured using targeted proteomics (Olink). Coregulation and cluster analysis of plasma proteins was performed using a training-set and confirmed in a test-set. An independent external cohort of 219 COVID-19 patients was used for validation and outcome analysis. Microcirculation and plasma proteome analysis found substantial overlap between COVID-19 and bacterial sepsis. Severity, but not disease entity explained most data variation. Unsupervised correlation analysis identified two main coregulated plasma protein signatures in both diseases that strictly counteract each other. They were associated with microvascular dysfunction and several established markers of clinical severity. The signatures were used to derive new composite biomarkers of microvascular injury that allow to predict 28-day mortality or/and intubation (area under the curve 0.90, p < 0.0001) in COVID-19.


Our data imply a common biological host response of microvascular injury in both bacterial sepsis and COVID-19. A distinct plasma signature correlates with endothelial health and improved outcomes, while a counteracting response is associated with glycocalyx breakdown and high mortality. Microvascular health biomarkers are powerful predictors of clinical outcomes.

Graphical abstract


Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, evidence has emerged that COVID-19 is a vascular rather than purely respiratory illness [1]. While direct viral infection of endothelial cells (ECs) with severe acute respiratory syndrome coronavirus 2 is controversial [2,3,4], some evidence indicates that EC injury in severely ill COVID-19 patients is secondary to the systemic inflammatory host response [1].

Numerous studies have investigated inflammatory markers in patients with severe COVID-19 and identified several biomarkers potentially associated with outcome. Among them are powerful activators of the endothelium such as interleukin (IL) 6, tumor necrosis factor (TNF-) α, vascular endothelial grow factor (VEGF-) A, and angiopoietin (Angpt-) 2. They are known to facilitate vascular hyperpermeability and microthrombosis leading to acute respiratory distress syndrome in severe COVID-19 [5,6,7,8,9,10,11]. Thus, the concept of a systemic inflammatory host response that drives the microthrombotic phenotype seen in COVID-19, is widely accepted.

Microvascular dysfunction is a hallmark of inflammation in bacterial sepsis [12]. Although COVID-19 and bacterial sepsis are distinct conditions, we hypothesized a common biological response that mediates microvascular damage. The aim of this proof-of-concept study was to compare microvascular injury and plasma proteins in patients with COVID-19 and bacterial sepsis to find differences and similarities between both inflammatory diseases.


Study design and study population

The current multi-center, prospective, observational, cross-sectional study took place in the University Hospital Münster and three local academic teaching hospitals. After written informed consent was obtained, adult hospitalized patients needing admission to the intensive care units (ICU-critical disease) and intermediate care wards (IMC-moderate/severe disease) because of COVID-19 infection or confirmed bacterial sepsis (sepsis-3 definition) [13] were prospectively enrolled in a non-consecutive manner. After initial resuscitation sublingual videomicroscopy was performed. Plasma samples were obtained, centrifuged, and stored at − 80 °C until analysis. Exclusion criteria were pregnancy or local oral mucosal inflammation. Ten healthy randomly selected age-matched volunteers served as controls, after undergoing a thorough clinical examination and laboratory blood tests. Some of the participants were already included in previous microvascular studies [9, 14, 15]. This study was approved by the relevant ethics committee (amendments of 2016–073-f-S) and was performed in accordance with the Declaration of Helsinki.

In vivo assessment of sublingual microcirculation and glycocalyx dimensions

A sidestream dark field camera (CapiScope HVCS, KK Technology, Honiton, UK) coupled with GlycoCheck™ software (Microvascular Health Solutions Inc., Alpine, UT, USA) was used to visualize passing red blood cells (RBCs) in the sublingual microvasculature (microvessels’ diameter 4–25 µm) at the bedside as previously described in detail [15, 16]. Based on the RBC dynamics in the valid vascular segments, the software calculates the following variables, which were successfully validated in the past [14, 15, 17]:

Perfused boundary region (PBR, in µm) expresses the dynamic lateral movement of RBCs into the permeable part of the endothelial glycocalyx layer, an inverse parameter of endothelial glycocalyx dimensions. The higher the PBR values, the more diminished the glycocalyx dimensions.

Capillary density (in 10−2 mm/mm2) was defined as the vascular density of vessels with a diameter ≤ the diameter of a single RBC (diameter ~ 7–8 µm [18]; capillary density diameter ≤ 7 µm).

RBC velocity (in µm/sec) can be determined in individual vessel segments in an automatic manner via cross correlation of longitudinal RBC intensity profiles between frames of recorded videos.

The combination of microcirculation and glycocalyx variables enables the estimation of microvascular health score MVHSstatic; higher values indicate healthier microvasculature.

Targeted plasma proteomics, circulating glycocalyx markers and angiopoietin-2

The Olink (Uppsala, Sweden) “inflammation1” and “cardiovascular2” proteomic panels comprise 92 proteins each. Seven proteins are common to both panels. A total of 184 proteins in 75 samples (COVID-19, bacterial sepsis, healthy) were measured in one batch to avoid technical variation. Briefly, the Olink proximity extension assay uses two specific oligonucleotide-labeled antibodies per protein (“probes”). When the two probes are in proximity a new PCR target sequence is formed via a proximity-dependent DNA polymerization event. The resulting sequence is subsequently detected and quantified using standard real-time quantitative PCR, as previously reported [19]. Measurements were conducted in triplicate. Results are reported as arbitrary units on a log2 scale. The proteins included in each panel, measurement details, and validation data are available online (

Plasma levels of the glycocalyx-core protein syndecan-1 (Diaclone, Besançon, France) and the endothelial-specific proinflammatory mediator Angpt-2 (R&D Systems, Minneapolis, USA) were measured using commercially available enzyme-linked immunosorbent assay kits in accordance with the manufacturer’s instructions.

External validation: Massachusetts General Hospital COVID-19 cohort

The proteomic signature derived from the study cohort was validated in a public database of adult COVID-19 patients admitted to Massachusetts General Hospital (MGH, Boston, Massachusetts, USA, [20]. The inclusion criteria were clinical concern of COVID-19 upon emergency department admission, and acute respiratory distress with at least one of the following: tachypnea ≥ 22 breaths/minute; oxygen saturation ≤ 92% on room air; requirement for supplemental oxygen; positive-pressure ventilation. The primary study outcome was a composite endpoint of 28-day mortality and/or intubation, and a total of 219 blood samples drawn on day 3 were assessed in conjunction with the primary study outcome.

Statistical analysis

Data are presented as absolute numbers, percentages, and medians with corresponding 25th and 75th percentiles (interquartile ranges; IQRs), as appropriate. The non-parametric Mann–Whitney-U and Kruskal–Wallis test with Dunn’s multiple comparisons, and the chi-square test were used to compare groups, as appropriate. To correct for multiple testing in comparisons of proteome data between bacterial sepsis and COVID-19, the false discovery rate approach of Benjamini–Hochberg was used, with a q-value of 1% deemed significant. Pearson’s correlation coefficient (r) was used to assess correlations between variables. Receiver-operating characteristic analysis was used to estimate areas under the curve (AUCs). All tests used were two-sided, and statistical significance was set at p < 0.05. SPSS (IBM Corporation, Armonk, NY, USA, v.26) and GraphPad Prism (GraphPad Prism Software Inc., San Diego, CA, USA, v.8.4.3) were used for statistical analyses.

Correlation analysis, principal component analysis and clustering

Pearson was used to correlate one protein with all other proteins using the proteomics dataset across all participants. The protein data were not normally distributed (Shapiro–Wilk test), thus Pearson’s correlation with bootstrapping (iteratively removing one participant) was applied (see Fig. 2 for details). There were no missing values in the dataset. The weakest correlation coefficient between two proteins after bootstrapping was displayed in a similarity matrix, whereby the circle size indicates the p value and the color indicates the correlation coefficient (red = positive, blue = negative). The matrix (rows and columns) was clustered by euclidean distance with average linkage.

The principal components in the multivariate dataset were calculated using singular value decomposition with imputation (pre-normalized data, no transformation), and visualized using ClustVis [21].

To determine the optimal number of clusters in each data set, the elbow method was applied using k means. It uses the within-cluster sum of square as a function of the number of clusters and creates a plot whereby the elbow of the curve is considered the optimal threshold. This analysis yielded an optimal cluster number of 3 in the training, test and validation set (Supplemental Fig. 1).

Term enrichment, and functional annotation

All significant clusters were subjected to functional annotation and term enrichment analysis using Metascape [22], which allows functional enrichment analysis (GO/KEGG/Reactome terms, canonical pathways, hallmark gene sets) and overlap analysis across multiple gene/protein lists. It uses the hypergeometric test and Benjamini–Hochberg p value correction to identify the terms that contain a greater number of genes of an input list than expected by chance. A subset of representative terms was selected and converted into a network layout. Each term is represented by a node, whose size represents the number of input proteins of that term. Terms with a similarity score > 0.3 are linked by an edge, and edge thickness is proportional to the similarity score. The network was visualized with Cytoscape (v.3.1.2) [23].


Our dataset consisted of 43 patients with bacterial sepsis and 22 with COVID-19 (Table 1, Supplemental Table 1). There were no significant differences between median [IQR] age (68 [57–79] vs. 63 [52–76] years, p = 0.12), sex (p = 0.14), or disease severity (Sequential Organ Failure Assessment [SOFA] score 9 [4–12] vs. 6 [2–12], p = 0.22) in the two groups. Ten healthy individuals served as controls.

Table 1 Baseline characteristics

Microvascular phenotyping by quantitative sublingual video microscopy

The perfused boundary region (PBR) is a surrogate marker for glycocalyx breakdown as measured by sublingual video microscopy. Bacterial sepsis patients exhibited significantly higher PBR4–25 µm values (i.e., thinner endothelial glycocalyx) than COVID-19 patients and healthy controls (Fig. 1A). However, severely ill COVID-19 and bacterial sepsis patients (dichotomized by the overall median SOFA score of ≥ 8) showed a similar PBR4–25 µm increase, indicating that the severity of inflammation associates with glycocalyx injury in both conditions (Fig. 1B). This finding was further corroborated by equally elevated plasma levels of shed syndecan-1 in both patient groups (Fig. 1C).

Both bacterial sepsis and COVID-19 patients exhibited > 50% reductions in capillary density, and significantly lower RBC velocity in feed vessels compared to healthy controls (Fig. 1D, E). The median [IQR] MVHSstatic was similarly decreased in both diseases compared to controls (bacterial sepsis 1.37 [0.96–2.25] vs. COVID-19 1.24, [0.80–2.67], p = 0.61) (Fig. 1F). Together, the sublingual microscopy data suggest similar patterns and severity of microvascular dysfunction in bacterial sepsis and COVID-19.

Fig. 1
figure 1

Microvascular phenotyping by quantitative sublingual videomicroscopy and in vitro glycocalyx markers in patients with bacterial sepsis (blue) or COVID-19 (red). Boxplots of perfused boundary region (PBR) based on A disease entity and B disease severity. C Boxplots of syndecan-1, a circulating glycocalyx marker. Boxplots of D capillary density (4–7 µm), E red blood cell velocity in the feed vessels, and F microvascular health score (MVHS). Ten apparently healthy subjects (green) were used as controls. ns not significant. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

Unsupervised correlation analysis of plasma proteomics

We performed targeted proteomics of 184 inflammation- and metabolism-related proteins using blood plasma. The principal component (PC) analysis revealed substantial overlap between bacterial sepsis and COVID-19 patients, but a clear distinction between healthy controls and patients (Fig. 2A, Supplemental Fig. 2). In line with this finding, after correcting for multiple tests only 7 proteins changed significantly in COVID-19 patients compared to bacterial sepsis (Table 2).

Fig. 2
figure 2

Unsupervised systems analysis to identify coregulated network responses. A Principal component (PC) analysis. The ellipses show a probability of 95% that a new datapoint from the same group is located inside the ellipse. B Overview of the workflow. Twenty-two COVID-19 and forty-three bacterial sepsis patients were divided into a matched training-set and a matched test-set. Ten apparently healthy individuals were used as controls. C Pearson correlation coefficients of all parameters were calculated with a bootstrapping algorithm. Briefly, it iteratively calculates the Pearson’s correlation coefficient for each data matrix minus one sample. For n = x samples, x similarity matrices will be calculated each time excluding one sample. The correlation coefficient closest to “0” (the weakest correlation) in x correlations for one pair of parameters will be used as a result and the confidence level can be determined. To test the significance of each pairwise correlation a Student’s t-distribution was calculated with a significance threshold of 0.05. The final result of the 184 proteins in the training-set was plotted as a similarity matrix of all serum proteins with the color indicating the correlation coefficient (red = high positive correlation, blue = high negative correlation) and the dot size indicating the significance. Significance was calculated using the two-sided t-test and is expressed as square size. D Cluster overlap between the training-set and the test-set. The three main clusters identified in the training-set remained significant in both the training-set and the test-set (coregulated protein clusters in the test-set and the external validation cohort are shown in Supplemental Figs. 3 and 4, respectively)

Table 2 Proteins showing a difference between bacterial sepsis and COVID-19

As we found vast similarities between COVID-19 and sepsis using microvascular and proteomic data, we hypothesized a similar biological host response. To elucidate co-regulated protein signatures, we applied unsupervised correlation and clustering analysis (workflow overview—Fig. 2B). To generate a test and training dataset, all patients were matched by SOFA score, resulting into two groups (Fig. 2B). There were no significant differences in clinical or laboratory data (Supplemental Table 2).

After plotting correlation coefficients as a heat map (184 × 184 proteins) and sorting by euclidean distance with average linkage, small and large coregulated protein clusters emerged along the diagonal that reflected mostly positive (red) and few negative (blue) correlations (Fig. 2C). Three main clusters (hereafter named cluster 1 to 3) emerged using the training-set. The same analysis was performed independently with the test-set, which also resulted in three protein clusters with largely similar composition compared to the training-set (Fig. 2C, D). For external validation, an independently published Olink protemics dataset was used containing only COVID-19 patients (MGH cohort, see methods). Coregulated protein clusters showing significant overlap with clusters 1 and 2 of the test and training-set were identified (Supplemental Fig. 4). These data suggest a common host response in inflammatory disease with serum proteins forming coregulated signatures.

Functional annotation and term enrichment analysis

Cluster 1 contained a total of 23 unique proteins, including von Willebrand factor-cleaving protease (ADAMTS13), Angpt-1, and VEGF-D. Cluster 2 contained 100 unique proteins, including IL2, IL6, IL8, IL10, IL14, IL16 and various inflammatory mediators (Supplemental Table 3). Functional annotation and term enrichment analysis of the proteins in each cluster suggested specific biological functions and revealed the engagement of different pathways (Fig. 3). IL10/IL13/IL4 signaling and interferon gamma production were detected in cluster 2, whereas Ras, Ras-related protein 1, and mitogen-activated protein kinase signaling were enriched in cluster 1, among others. Cluster 3 did not show any clinical correlations (see below) and was excluded from the functional analysis.

Table 3 Correlations of cluster 1 (mean) and cluster 2 (mean) with clinical and laboratory variables
Fig. 3
figure 3

Significant pathways engaged in clusters 1 and 2 according to functional annotation and term enrichment analysis. Proteins from clusters 1 and 2 were subjected to functional annotation and term enrichment analysis using Metascape. The network was visualized with Cytoscape (v.3.1.2)

Associations between proteomic clusters and disease severity, disease entity, and microvascular parameters

As all proteins in each protein cluster are positively correlated, we calculated the mean cluster concentration of all cluster proteins for each patient. Interestingly, the normalized means of clusters 1 and 2 were negatively correlated with each other (r = − 0.52, p < 0.0001; Supplemental Fig. 5), suggesting opposite or intertwined biological responses. The means of cluster 3 did not correlate with microvascular, laboratory or clinical parameters (Supplemental Fig. 6, Table 3) and therefore was excluded from further analysis. Regardless of the SOFA score, there were no significant associations between clusters 1 and 2 and the disease entity (bacterial sepsis/COVID-19) (Supplemental Fig. 7A, C).

Cluster 1 and 2 correlated significantly with several established markers of critical/acute illness in sepsis and COVID-19 patients, including mean arterial pressure, C-reactive protein, ferritin, creatinine, norepinephrine dose and SOFA score (Table 3). For example, cluster 1 was negatively correlated with the SOFA score (r = − 0.66, p < 0.0001), whereas cluster 2 was positively correlated (r = 0.74, p < 0.0001) (Supplemental Fig. 7B, D). Similar findings were made regarding the number of dysfunctional organ systems (Supplemental Fig. 8) and microvascular measurements. Both protein clusters were moderately correlated with capillary density and PBR4–25 µm (Supplemental Fig. 6). In line with these data, both clusters were strongly correlated with the glycocalyx breakdown-associated proteins syndecan-1 and Angpt-2 (Table 3). Again, cluster 1 was positively correlated with microvascular health and negatively correlated with microvascular injury; and cluster 2 vice versa. These findings suggest that glycocalyx integrity is linked to the systemic host response during inflammatory disease.

Derivation of a composite biomarker of microvascular dysfunction

A second approach was to identify individual proteins that correlate with microvascular measurements. Therefore, we plotted the Pearson correlation coefficients of PBR4–25 µm (Fig. 4A) and capillary density (Fig. 4B) with each of the 184 proteins in ascending order. After coloring the cluster membership of each protein (as shown in Fig. 2), most cluster 1 proteins (green) correlated with low PBR and high capillary density, indicating a healthy microcirculation. In contrast, most cluster 2 proteins (yellow) correlated with high PBR and low capillary density, indicating microvascular dysfunction. This approach highlights that microvascular injury is mirrored by specific changes in the serum proteome.

Fig. 4
figure 4

Derivation of a proteomic signature (“Microcode”) of microvascular dysfunction. Correlations between the 184 proteins in the proteomics analysis and A PBR 4–25 μm and B capillary density in a ranked manner. Cluster 1 (green) correlates with intact microcirculation (low PBR, high capillary density) and cluster 2 (yellow) correlates with damaged microcirculation (high PBR, low capillary density). C Combination of the “top eight” proteins (four per cluster) derive a proteomic signature (“Microcode”) of microvascular dysfunction. The proteins of each cluster were normalized and their quotient was calculated per subject using the formula: \({{{\text{Cluster}}\,1\,{\text{proteins}}} \mathord{\left/ {\vphantom {{{\text{Cluster}}\,1\,{\text{proteins}}} {{\text{Cluster}}\,2\,{\text{proteins}}}}} \right. \kern-\nulldelimiterspace} {{\text{Cluster}}\,2\,{\text{proteins}}}}\).. D Correlation of “Microcode”values with SOFA score in bacterial sepsis and COVID-19 patients. E Boxplots showing median [IQR] “Microcode” values and disease severity and entity. ns not significant. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

We next aimed to develop a composite biomarker based on plasma proteins that correspond to microvascular health. Given that cluster 1 and cluster 2 were inversely correlated, the following quotient served as a proteomic signature of microvascular dysfunction (hereafter referred to as “Microcode”) (Fig. 4C):

$${\text{Top}}{{\,{\text{Cluster}}\,1\,{\text{proteins}}} \mathord{\left/ {\vphantom {{\,{\text{Cluster}}\,1\,{\text{proteins}}} {{\text{Top}}\,{\text{Cluster}}\,2\,{\text{proteins}}}}} \right. \kern-\nulldelimiterspace} {{\text{Top}}\,{\text{Cluster}}\,2\,{\text{proteins}}}} .$$

Because cluster 1 decreased with disease severity and cluster 2 increased, the Microcode values decreased with increasing microvascular dysfunction. Microcode values were not different between COVID-19 and bacterial sepsis, but associated with disease severity in both diseases (Fig. 4D-E). As our study was not designed for outcome analysis, we used the external MGH cohort.

“Microcode” predicts outcome in the validation set

Microcode was then applied to the external COVID-19 validation cohort (MGH), which annotates proteome and outcome data. Regarding the composite endpoint 28-day mortality and/or intubation, Microcode performed similar to the known COVID-19 biomarker IL6 [24] (AUC [95% CI] 0.90 [0.86–0.94, p < 0.0001] vs. IL6 0.88 [0.83–0.92, p < 0.0001]) (Fig. 5A). Low Microcode values associated with higher D-dimer levels, a complicated clinical course, and a poorer outcome (Fig. 5B–D). Of note, proteomic signatures for PBR and capillary density associated independently with disease severity (Supplemental Fig. 9).

Fig. 5
figure 5

External validation of “Microcode” signature in an independent COVID-19 cohort from Massachusetts General Hospital (n = 219). A Receiver-operating characteristic curves showing the predictive capacity of the Microcode. BD Boxplots of Microcode values classified based on B correspondent D-dimer values, C the worst outcome in the 28 days of hospitalization, and D the composite endpoint of 28-day mortality and/or intubation. AU arbitrary units, ns not significant. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001


The current analysis demonstrates substantial overlap of both, microvascular and proteomic phenotypes in critically ill patients with COVID-19 and bacterial sepsis. To date very few studies have investigated differences and similarities between COVID-19 and bacterial sepsis. Regarding the inflammatory and immune response, the results are inconsistent, presumably because the two entities are inherently difficult to match [25,26,27,28]. In the present study, we observed differences in the routine laboratory data; bacterial sepsis patients showed higher procalcitonin (PCT) and C-reactive protein (CRP) values, while COVID-19 patients showed higher ferritin values. These differences have been reported previously and might be attributed to the alternative pathophysiological models of organ dysfunction between the two entities. Briefly, compared to bacterial sepsis, SARS-CoV-2 probably causes a direct viral injury, associates with lymphopenia and is responsible for a virus-induced immunosuppression [29]. In line with past studies, IL-6 levels were significantly higher in our sepsis cohort compared to COVID-19 [30, 31]. However, IL-6 receptor blockers (e.g., tocilizumab) suppressing the so-called cytokine storm are still recommended in COVID-19 patients with systemic inflammation [32], but not in bacterial sepsis.

Despite the above observed differences, our data clearly show that microvascular and proteome signatures of both diseases were very similar. In both entities glycocalyx and microcirculatory impairment were associated with clinical severity and specific proteome signatures. Both COVID-19 and bacterial sepsis patients exhibited virtually the same changes when compared to healthy controls. These concordant deviations from the healthy state were identified via unsupervised systems analysis and clustered into two groups, each of which correlated with disease severity. The correlation between a proinflammatory protein response (cluster 2) and microvascular injury was not surprising, but another signature (cluster 1) was also identified that corresponded with microvascular health. Our data imply a system’s biology concept with co-regulated host responses across different inflammatory disease. This also allowed to develop composite biomarkers that predicted mortality in an external, independent cohort of COVID-19 patients. To our knowledge, this is the first clinical biomarker that was derived from microvascular health parameters.

Our novel approach of the microcirculation enabled us to compare the microvascular phenotype of COVID-19 patients with that of bacterial sepsis patients, and to correlate proteomic data with PBR and capillary density. Although individual protein correlations were only moderate, a prototypical composite biomarker consisting of several proteins with the strongest associations was able to predict outcome in an independent, external COVID-19 cohort. This constitutes proof-of-concept that the devised Microcode signature can be used for risk stratification in COVID-19 patients. As there is no corresponding public proteome library derived from bacterial sepsis patients, it is not possible to test the microvascular signature in a dedicated bacterial sepsis cohort.

Notably the protein signature identified is purely associative, and no causal relationships can be inferred. Most proteins in the prototypical signature have been attributed to the regulation of different components of the immune system, and only hepatocyte growth factor and TNF-β have been studied in relation to vascular barriers and endothelial function, respectively [33,34,35,36]. Among the other candidates significantly correlated with glycocalyx thickness or capillary density there were several proteins with strong vascular connections, such as VEGF, Angpt-1, and ADAMTS13. These candidates, as well as other pathways identified via functional annotation and term enrichment analysis, can be used as a reasonable starting point for planning further mechanistic experiments.

The current study had some limitations. First, the inclusion of patients in the training and test cohorts represents a cross-sectional design. Although the wide range of patients with varying disease severity is certainly advantageous with respect to correlation analysis, this design was not suitable for the analysis of clinical outcomes. Second, the proteome data contains mainly a priori selected vascular and inflammatory proteins and does not represent the entire patients’ proteome. Third, we cannot exclude that the lack of differences between COVID-19 and bacterial sepsis might have been partially influenced by the limited sample size. However, our study is hypothesis generating and Microcode should be further confirmed in future trials. Fourth, it is unclear how representative and robust our Microcode signature is regarding outcome prediction over time, because patients were only enrolled in a cross-sectional fashion after initial resuscitation of bacterial sepsis or COVID-19-related hospitalization. Fifth, although routine microbiological sampling was performed in the COVID-19 group, we cannot exclude the possibility of bacterial superinfections in the COVID-19 group, which may have partially influenced the results. However, the prevalence of bacterial co-infections in COVID-19 is considered rather low [37, 38] and our COVID-19 cohort had a low median PCT value (0.6 ng/ml), which argues against overt co-infections.

Conclusion and outlook

Despite the above limitations the data clearly indicate that COVID-19 and bacterial sepsis share common proteomic signatures and features of microvascular damage. Integrating multi-omic data in clinical studies is a promising approach to decipher systemic host responses and microvascular damage, and develop new diagnostic and therapeutic concepts in inflammatory disease.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Data of the external cohort were provided by the MGH Emergency Department COVID-19 Cohort (Filbin, Goldberg, Hacohen) with Olink Proteomics and are available online.





Area under the curve


Coronavirus disease 2019


C-reactive protein


Endothelial cell




Interquartile range


Microvascular health score


Perfused boundary region


Principal component




Red blood cell


Sequential Organ Failure Assessment


Tumor necrosis factor


Vascular endothelial growth factor


  1. Smadja DM, Mentzer SJ, Fontenay M, Laffan MA, Ackermann M, Helms J, Jonigk D, Chocron R, Pier GB, Gendron N, Pons S, Diehl JL, Margadant C, Guerin C, Huijbers EJM, Philippe A, Chapuis N, Nowak-Sliwinska P, Karagiannidis C, Sanchez O, Kumpers P, Skurnik D, Randi AM, Griffioen AW (2021) COVID-19 is a systemic vascular hemopathy: insight for mechanistic and clinical aspects. Angiogenesis.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Goldsmith CS, Miller SE, Martines RB, Bullock HA, Zaki SR (2020) Electron microscopy of SARS-CoV-2: a challenging task. The Lancet 395(10238):e99.

    CAS  Article  Google Scholar 

  3. McCracken IR, Saginc G, He L, Huseynov A, Daniels A, Fletcher S, Peghaire C, Kalna V, Andaloussi-Mäe M, Muhl L, Craig NM, Griffiths SJ, Haas JG, Tait-Burkard C, Lendahl U, Birdsey GM, Betsholtz C, Noseda M, Baker AH, Randi AM (2021) Lack of evidence of ACE2 expression and replicative infection by SARSCoV-2 in human endothelial cells. Circulation.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bradley BT, Maioli H, Johnston R, Chaudhry I, Fink SL, Xu H, Najafian B, Deutsch G, Lacy JM, Williams T, Yarid N, Marshall DA (2020) Histopathology and ultrastructural findings of fatal COVID-19 infections in Washington State: a case series. Lancet 396(10247):320–332.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China Lancet 395(10223):497–506

    CAS  Article  Google Scholar 

  6. Smadja DM, Guerin CL, Chocron R, Yatim N, Boussier J, Gendron N, Khider L, Hadjadj J, Goudot G, Debuc B, Juvin P, Hauw-Berlemont C, Augy J-L, Peron N, Messas E, Planquette B, Sanchez O, Charbit B, Gaussem P, Duffy D, Terrier B, Mirault T, Diehl J-L (2020) Angiopoietin-2 as a marker of endothelial activation is a good predictor factor for intensive care unit admission of COVID-19 patients. Angiogenesis 23(4):611–620.

    CAS  Article  PubMed  Google Scholar 

  7. Bermejo-Martin JF, Gonzalez-Rivera M, Almansa R, Micheloud D, Tedim AP, Dominguez-Gil M, Resino S, Martin-Fernandez M, Ryan Murua P, Perez-Garcia F, Tamayo L, Lopez-Izquierdo R, Bustamante E, Aldecoa C, Gomez JM, Rico-Feijoo J, Orduna A, Mendez R, Fernandez Natal I, Megias G, Gonzalez-Estecha M, Carriedo D, Doncel C, Jorge N, Ortega A, de la Fuente A, Del Campo F, Fernandez-Ratero JA, Trapiello W, Gonzalez-Jimenez P, Ruiz G, Kelvin AA, Ostadgavahi AT, Oneizat R, Ruiz LM, Miguens I, Gargallo E, Munoz I, Pelegrin S, Martin S, Garcia Olivares P, Cedeno JA, Ruiz Albi T, Puertas C, Berezo JA, Renedo G, Herran R, Bustamante-Munguira J, Enriquez P, Cicuendez R, Blanco J, Abadia J, Gomez Barquero J, Mamolar N, Blanca-Lopez N, Valdivia LJ, Fernandez Caso B, Mantecon MA, Motos A, Fernandez-Barat L, Ferrer R, Barbe F, Torres A, Menendez R, Eiros JM, Kelvin DJ (2020) Viral RNA load in plasma is associated with critical illness and a dysregulated host response in COVID-19. Crit Care 24(1):691.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Pine AB, Meizlish ML, Goshua G, Chang CH, Zhang H, Bishai J, Bahel P, Patel A, Gbyli R, Kwan JM, Won CH, Price C, Dela Cruz CS, Halene S, van Dijk D, Hwa J, Lee AI, Chun HJ (2020) Circulating markers of angiogenesis and endotheliopathy in COVID-19. Pulm Circ 10(4):2045894020966547

    Article  Google Scholar 

  9. Rovas A, Osiaevi I, Buscher K, Sackarnd J, Tepasse PR, Fobker M, Kuhn J, Braune S, Gobel U, Tholking G, Groschel A, Pavenstadt H, Vink H, Kumpers P (2020) Microvascular dysfunction in COVID-19: the MYSTIC study. Angiogenesis.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Smadja DM, Philippe A, Bory O, Gendron N, Beauvais A, Gruest M, Peron N, Khider L, Guerin CL, Goudot G, Levavasseur F, Duchemin J, Pene F, Cheurfa C, Szwebel TA, Sourdeau E, Planquette B, Hauw-Berlemont C, Hermann B, Gaussem P, Samama CM, Mirault T, Terrier B, Sanchez O, Rance B, Fontenay M, Diehl JL, Chocron R (2021) Placental growth factor level in plasma predicts COVID-19 severity and in-hospital mortality. J Thromb Haemost.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rauch A, Dupont A, Goutay J, Caplan M, Staessens S, Moussa M, Jeanpierre E, Corseaux D, Lefevre G, Lassalle F, Faure K, Lambert M, Duhamel A, Labreuche J, Garrigue D, De Meyer SF, Staels B, Van Belle E, Vincent F, Kipnis E, Lenting PJ, Poissy J, Susen S, Lille CRN, Members of the LSC (2020) Endotheliopathy is induced by plasma from critically ill patients and associated with organ failure in severe COVID-19. Circulation 142(19):1881–1884.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Ince C, Boerma EC, Cecconi M, De Backer D, Shapiro NI, Duranteau J, Pinsky MR, Artigas A, Teboul JL, Reiss IKM, Aldecoa C, Hutchings SD, Donati A, Maggiorini M, Taccone FS, Hernandez G, Payen D, Tibboel D, Martin DS, Zarbock A, Monnet X, Dubin A, Bakker J, Vincent JL, Scheeren TWL (2018) Second consensus on the assessment of sublingual microcirculation in critically ill patients: results from a task force of the European Society of Intensive Care Medicine. Intensive Care Med 44(3):281–299.

    Article  PubMed  Google Scholar 

  13. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC (2016) The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315(8):801–810.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Drost CC, Rovas A, Kusche-Vihrog K, Van Slyke P, Kim H, Hoang VC, Maynes JT, Wennmann DO, Pavenstadt H, Linke W, Lukasz A, Hesse B, Kumpers P (2019) Tie2 activation promotes protection and reconstitution of the endothelial glycocalyx in human sepsis. Thromb Haemost 119(11):1827–1838.

    Article  PubMed  Google Scholar 

  15. Rovas A, Seidel LM, Vink H, Pohlkotter T, Pavenstadt H, Ertmer C, Hessler M, Kumpers P (2019) Association of sublingual microcirculation parameters and endothelial glycocalyx dimensions in resuscitated sepsis. Crit Care 23(1):260.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rovas A, Lukasz AH, Vink H, Urban M, Sackarnd J, Pavenstadt H, Kumpers P (2018) Bedside analysis of the sublingual microvascular glycocalyx in the emergency room and intensive care unit: the GlycoNurse study. Scand J Trauma Resusc Emerg Med 26(1):16.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Rovas A, Sackarnd J, Rossaint J, Kampmeier S, Pavenstadt H, Vink H, Kumpers P (2021) Identification of novel sublingual parameters to analyze and diagnose microvascular dysfunction in sepsis: the NOSTRADAMUS study. Crit Care 25(1):112.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Diez-Silva M, Dao M, Han J, Lim CT, Suresh S (2010) Shape and biomechanical characteristics of human red blood cells in health and disease. MRS Bull 35(5):382–388.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Assarsson E, Lundberg M, Holmquist G, Bjorkesten J, Thorsen SB, Ekman D, Eriksson A, Rennel Dickens E, Ohlsson S, Edfeldt G, Andersson AC, Lindstedt P, Stenvang J, Gullberg M, Fredriksson S (2014) Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE 9(4):e95192.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Filbin MR, Mehta A, Schneider AM, Kays KR, Guess JR, Gentili M, Fenyves BG, Charland NC, Gonye ALK, Gushterova I, Khanna HK, LaSalle TJ, Lavin-Parsons KM, Lilley BM, Lodenstein CL, Manakongtreecheep K, Margolin JD, McKaig BN, Rojas-Lopez M, Russo BC, Sharma N, Tantivit J, Thomas MF, Gerszten RE, Heimberg GS, Hoover PJ, Lieb DJ, Lin B, Ngo D, Pelka K, Reyes M, Smillie CS, Waghray A, Wood TE, Zajac AS, Jennings LL, Grundberg I, Bhattacharyya RP, Parry BA, Villani AC, Sade-Feldman M, Hacohen N, Goldberg MB (2021) Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions. Cell Rep Med 2(5):100287.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Metsalu T, Vilo J (2015) ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res 43(W1):W566-570.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10(1):1523.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Del Valle DM, Kim-Schulze S, Huang HH, Beckmann ND, Nirenberg S, Wang B, Lavin Y, Swartz TH, Madduri D, Stock A, Marron TU, Xie H, Patel M, Tuballes K, Van Oekelen O, Rahman A, Kovatch P, Aberg JA, Schadt E, Jagannath S, Mazumdar M, Charney AW, Firpo-Betancourt A, Mendu DR, Jhang J, Reich D, Sigel K, Cordon-Cardo C, Feldmann M, Parekh S, Merad M, Gnjatic S (2020) An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat Med 26(10):1636–1643.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Wilson JG, Simpson LJ, Ferreira AM, Rustagi A, Roque J, Asuni A, Ranganath T, Grant PM, Subramanian A, Rosenberg-Hasson Y, Maecker HT, Holmes SP, Levitt JE, Blish CA, Rogers AJ (2020) Cytokine profile in plasma of severe COVID-19 does not differ from ARDS and sepsis. JCI Insight.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Dong X, Wang C, Liu X, Gao W, Bai X, Li Z (2020) Lessons learned comparing immune system alterations of bacterial sepsis and SARS-CoV-2 sepsis. Front Immunol 11:598404.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Kox M, Frenzel T, Schouten J, van de Veerdonk FL, Koenen H, Pickkers P, Group obotRCIC-s (2020) COVID-19 patients exhibit less pronounced immune suppression compared with bacterial septic shock patients. Crit Care 24(1):263.

    Article  PubMed  PubMed Central  Google Scholar 

  28. de Roquetaillade C, Mansouri S, Brumpt C, Neuwirth M, Voicu S, Le Dorze M, Fontaine C, Barthelemy R, Gayat E, Megarbane B, Mebazaa A, Chousterman BG (2021) Comparison of circulating immune cells profiles and kinetics between coronavirus disease 2019 and bacterial sepsis. Crit Care Med.

    Article  PubMed  Google Scholar 

  29. Leisman DE, Ronner L, Pinotti R, Taylor MD, Sinha P, Calfee CS, Hirayama AV, Mastroiani F, Turtle CJ, Harhay MO, Legrand M, Deutschman CS (2020) Cytokine elevation in severe and critical COVID-19: a rapid systematic review, meta-analysis, and comparison with other inflammatory syndromes. Lancet Respir Med 8(12):1233–1244.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Yu J, Wang Y, Lin S, Jiang L, Sang L, Zheng X, Zhong M (2021) Severe COVID-19 has a distinct phenotype from bacterial sepsis: a retrospective cohort study in deceased patients. Ann Transl Med 9(13):1054.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Perschinka F, Mayerhofer T, Lehner GF, Hasslacher J, Klein SJ, Joannidis M (2022) Immunologic response in bacterial sepsis is different from that in COVID-19 sepsis. Infection.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Health NIo (2022) Coronavirus Disease 2019 (COVID-19) Treatment Guidelines. Accessed 7 May 2022

  33. Shinjo K, Tsuda S, Hayami T, Asahi T, Kawaharada H (1989) Increase in permeability of human endothelial cell monolayer by recombinant human lymphotoxin. Biochem Biophys Res Commun 162(3):1431–1437.

    CAS  Article  PubMed  Google Scholar 

  34. Wassmer SC, Combes V, Candal FJ, Juhan-Vague I, Grau GE (2006) Platelets potentiate brain endothelial alterations induced by Plasmodium falciparum. Infect Immun 74(1):645–653.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Meng SS, Guo FM, Zhang XW, Chang W, Peng F, Qiu HB, Yang Y (2019) mTOR/STAT-3 pathway mediates mesenchymal stem cell-secreted hepatocyte growth factor protective effects against lipopolysaccharide-induced vascular endothelial barrier dysfunction and apoptosis. J Cell Biochem 120(3):3637–3650.

    CAS  Article  PubMed  Google Scholar 

  36. Liu F, Schaphorst KL, Verin AD, Jacobs K, Birukova A, Day RM, Bogatcheva N, Bottaro DP, Garcia JG (2002) Hepatocyte growth factor enhances endothelial cell barrier function and cortical cytoskeletal rearrangement: potential role of glycogen synthase kinase-3beta. FASEB J 16(9):950–962.

    CAS  Article  PubMed  Google Scholar 

  37. Langford BJ, So M, Raybardhan S, Leung V, Westwood D, MacFadden DR, Soucy JR, Daneman N (2020) Bacterial co-infection and secondary infection in patients with COVID-19: a living rapid review and meta-analysis. Clin Microbiol Infect 26(12):1622–1629.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Adler H, Ball R, Fisher M, Mortimer K, Vardhan MS (2020) Low rate of bacterial co-infection in patients with COVID-19. Lancet Microbe 1(2):e62.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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We would like to acknowledge the support of the Open Access Publication Fund of the University of Münster.


Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Innovative Medical Research Fund of the University of Münster Medical School (I-RO221907, to AR and KB), and by the German Research Foundation (rotational position of KFO 342 – ZA428/18-1 to AR and CD; KU 2873/3-1 to PK, RO 4537/5-1 to JR and LU-2321/1-1 to AL). The funding sources had no role in study design, data collection and analysis, the decision to publish, or preparation of the manuscript.

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Authors and Affiliations



AR conceptualized the study, designed the study, performed/supervised the execution of sublingual video microscopy, performed blood sampling and measurement, analyzed microvascular data, prepared figures, and drafted the manuscript. KB designed the study, performed proteomic analysis, prepared figures, and contributed to the manuscript. IO performed sublingual video microscopy, and blood sampling and measurement, and revised the manuscript. JS, PRT, JK, SB, UG, GT, AG, AL, and HP contributed to the design of the study, discussed the findings, revised the manuscript, and/or enrolled patients. MF contributed to laboratory measurements, discussed the data, and revised the manuscript. HV analyzed video microscopy data offline, advised on the microcirculation analysis, discussed the data, and revised the manuscript. PK conceptualized the study, designed, and supervised the study, recruited, and coordinated participating centers, contributed to the analysis and figures, and drafted the manuscript.

Corresponding author

Correspondence to Philipp Kümpers.

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Conflict of interest

AR, KB, IO, CD, JS, PRT, MF, JK, SB, UG, GT, AG, JR, AL, HP, PK declare no competing interests. HV is Chief Science Officer of GlycoCheck BV, The Netherlands.

Ethical approval

The study was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the General Medical Council Westfalen-Lippe and the WWU Münster, Germany (File Number: amendments of 2016-073-f-S).

Informed consent

Patients were enrolled upon after obtaining written informed consent from them or their legal representatives.

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Rovas, A., Buscher, K., Osiaevi, I. et al. Microvascular and proteomic signatures overlap in COVID-19 and bacterial sepsis: the MICROCODE study. Angiogenesis (2022).

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  • Microcirculation
  • Microvascular dysfunction
  • Sepsis
  • COVID-19
  • Proteomic signature
  • Biomarker