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Clinical Aspects

Diagnostic Utility of Different Blood Components in Gene Expression Analysis of Sepsis

Maslove, David M.; Marshall, John C.

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doi: 10.1097/SHK.0000000000000511



Originally pioneered in cancer care and other chronic diseases, gene expression analysis is increasingly used in the investigation of illnesses such as sepsis and trauma (1). Transposing genome-wide techniques to the study of acute illnesses whose basis is physiologic rather than anatomic requires consideration of the appropriate tissue from which to isolate nucleic acids. For cancer, this choice is driven by the specific tissue type that gives rise to the tumor. Diseases such as sepsis impact a number of tissue types simultaneously. In these cases, the selection of a tissue type from which to generate gene expression profiles is uncertain. Investigators are therefore left with a decision that may be informed by cost, convenience, and current practices, but for which an evidentiary basis is limited.

Gene expression studies in sepsis have typically focused on two separate but related tasks. First, studies have aimed to identify specific gene expression states that correspond to the clinical definitions of severe sepsis and septic shock (2, 3). This approach is best illustrated in a recent study by Sweeney et al. in which an 11-gene sepsis gene set was identified by analyzing a number of publicly available gene expression datasets (4). The expression values of this 11-gene signature were shown to differentiate sepsis from sterile inflammation with a high degree of accuracy.

A second type of sepsis gene expression study addresses the lack of specificity inherent in the clinical definition of sepsis, which results in considerable heterogeneity among those assigned this diagnosis. Using an unsupervised, data-driven approach, these studies seek to determine if distinct subtypes of sepsis can be identified based on gene expression characteristics (5, 6).

For both of the above study aims, investigators have largely focused on blood and its cellular components to generate gene expression data (7). This choice reflects not only the ready availability of blood for easy and repeated sampling, but also the key role of blood leukocytes in modulating and effecting the immune and inflammatory responses to infection. Previous work has demonstrated cell-specific expression profiles that differ between leukocyte types, and that reflect their specialized roles in innate and adaptive immunity (8). Nonetheless, the question of which blood cell compartment is most useful in gene expression studies of sepsis has not been explored in depth.

In this study, we used bioinformatics methods to systematically examine gene expression studies of sepsis. Our main objective was to determine the relative utility of different blood cell compartments in studies investigating gene expression correlates of clinical sepsis definitions, as well as those aimed at identifying specific expression-defined sepsis subtypes. We employed a method designed to be agnostic to microarray platform so that samples from a variety of different studies could be included. We evaluated the potential of different blood-derived source tissues to differentiate between patients diagnosed with sepsis, and non-sepsis controls. We also assessed the utility of gene expression data from these tissue sources in unsupervised learning tasks, specifically the capacity to generate distinct patient clusters determined by expression patterns alone.


We searched the NCBI's Gene Expression Omnibus (GEO) using the search terms “sepsis” and “septic shock,” limiting results to human studies, and those with a dataset type of “expression profiling by array.” In order to select studies for further analysis, we reviewed the information contained in the GEO records of each study, as well as the abstracts and full text of any corresponding publications where available. We excluded studies focused on viral diseases such as HIV, studies on malaria, in vitro studies such as endotoxin stimulation assays, and studies enrolling patients with a primary diagnosis of trauma or burns. Studies that had no control patients were also excluded. For each of the included studies, we limited our analysis to samples that were taken within 24 h of the diagnosis of sepsis. This restriction reflects the dynamic nature of gene expression in sepsis syndromes, which has been shown to undergo marked fluctuations in the first few days of illness (9, 10).

For each dataset, we downloaded the normalized expression data, as well as metadata containing whatever clinical information was available on the patients from whom the samples were drawn. We extracted data describing each individual experiment, including the population studied, the tissue type from which the RNA was isolated, and the type of microarray platform used. Each experiment was processed and analyzed individually using its own controls.

We used sample variance as a method of feature selection in order to restrict the number of genes used in the analysis to those most likely to identify expression-based differences between patients. For each individual gene expression dataset, we calculated the variance across probe sets, and selected those with the highest variances (top percentile). We used a partitioning around medoids (PAM) algorithm to generate two clusters of samples, and compared the cluster labels (“A” or “B”) to the clinically assigned labels (“sepsis” or “control”). We evaluated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the cluster assignments relative to the clinical diagnosis of sepsis, as well as the proportion of correctly classified samples (accuracy). Cluster-derived class labels were assigned so as to maximize specificity for the diagnosis of sepsis.

For the unsupervised learning task, we again used a PAM algorithm to partition the samples from each individual study into clusters. The relative quality of clustering was determined by the average silhouette width, a measure that reflects both cluster cohesiveness and cluster separation (11). The range of possible values for the average silhouette width is between −1 and 1, with higher values suggesting better clustering. For each gene expression study, we calculated the average silhouette width for the samples divided into two, three, four, and five clusters, in order to evaluate whether more than two distinct subtypes could be supported by clustering of gene expression data.

We used principal components analysis (PCA) to visualize the gene expression data of select studies. We used Mann–Whitney and Kruskal–Wallis tests to compare the test performance characteristics and average silhouette widths between the tissue types examined. All analyses were done in R (version 3.1.1). Datasets were downloaded from NCBI GEO using the Bioconductor package GEOquery (12).


We identified 19 GEO series meeting our inclusion criteria, one of which included data from three different blood cell types, and one of which was divided across two different microarray platforms. A total of 22 experiments were therefore included in the analysis (Fig. 1 and Table, Supplemental Digital Content 1, at, encompassing a total of 1,765 unique gene expression records (5, 13–27). For most adult studies, sepsis was defined according to either the 1992 American College of Chest Physicians/Society of Critical Care Medicine definition or the 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions (28, 29). Most pediatric studies defined sepsis according to pediatric-specific consensus criteria (30). The Affymetrix Human Genome U133 Plus 2.0 array was used in 13 of the studies. Eleven studies were done in pediatric populations. Healthy controls were used in 14 studies, with non-septic ICU patients used as controls in five studies, and controls not further specified in the remaining studies. Whole blood was the most commonly used source tissue (15 studies), followed by peripheral blood mononuclear cells (PBMC, two studies), neutrophils (two studies), monocytes (two studies), and lymphocytes (one study). In subsequent analyses, the monocyte, lymphocyte, PBMC, and neutrophil groups were merged into a single group (hereafter referred to as the “leukocyte isolates” group). Variance-based feature selection led to the use of between 100 and 550 high variance probe sets in the analysis of each data set.

Fig. 1:
Flowchart illustrating the selection of studies for inclusion in the analysis.GEO indicates Gene Expression Omnibus.

The performance measures for gene expression data derived from whole blood and leukocyte isolates are shown in Figure 2. Both whole blood and leukocyte isolate gene expression data tended to show low sensitivity and negative predictive value for the diagnosis of sepsis, with better performance in specificity and positive predictive value. The two studies that used PBMC-derived gene expression data showed poor overall accuracy of classification for the diagnosis of sepsis. For lymphocyte-derived gene expression data, performance across the five studies was variable, with two studies yielding perfect classification (GSE46955 and GSE11755), and two studies showing poor performance (GSE9960 and GSE48080). Specificity was significantly higher with samples derived from whole blood compared with leukocyte isolates (mean specificity 94% vs 78%, P = 0.03).

Fig. 2:
Test performance characteristics for the diagnosis of sepsis using microarray data derived from different blood components.Whole blood-derived data showed greater specificity than data derived from leukocyte isolates (P < 0.05). NPV indicates negative predictive value; PPV, positive predictive value; Sens; sensitivity; Spec, specificity.

Specificity for the diagnosis of sepsis was higher in studies using healthy controls than in those using non-sepsis ICU patients as controls (94% vs 70%, P = 0.002). Though small sample sizes precluded a subgroup analysis, limiting the studies to those that used non-septic ICU patients as controls suggests that both tissue types yielded similar specificities for the diagnosis of sepsis (whole blood 72%, leukocyte isolates 69%).

In the unsupervised learning task, we used the average silhouette width as a measure of cluster cohesiveness in order to quantify how well gene expression data from the various cell types separated samples into distinct groups. Examples of highly clustered (i.e., higher average silhouette width) and poorly clustered (i.e., lower average silhouette width) datasets are shown in Figure 3. There was a significant difference in cluster cohesiveness between whole blood and leukocyte isolates, with whole blood-derived data yielding significantly higher average silhouette widths (median values 0.28 and 0.19 respectively, P = 0.002). In the specific task of forming two clusters, the difference in average silhouette width was again significant, with whole blood resulting in a higher value than leukocyte isolates (median values 0.41 vs 0.29, P < 0.05, Fig. 4). Studies using non-septic ICU patients as controls tended to form less cohesive clusters overall (median values 0.18 vs 0.30, P = 0.0001). Among the non-septic controls, whole blood-derived samples showed better cluster cohesion than samples derived from leukocyte isolates (median values 0.24 vs 0.14), although smaller sample sizes limited the statistical power of this subgroup analysis.

Fig. 3:
Principal components analysis (PCA) representations of gene expression data yielding clusters with differing average silhouette widths.The shapes of the points reflect the cluster assignment as determined by the partitioning around medoids (PAM) clustering algorithm used in the analysis. Filled shapes represent sepsis cases while open shapes are control samples, which in the case of the two studies shown were non-septic ICU patients. The left panel shows gene expression data derived from neutrophil RNA (GSE6535) that results in overlapping clusters, while the right panel shows gene expression data derived from whole blood RNA (GSE32707) that results in more cohesive clusters. These differences are reflected in the average silhouette widths (0.16 for the neutrophil-derived data vs 0.48 for whole blood-derived data). Clustering of whole blood data revealed a distinct subset of controls, whereas sepsis and control samples are intermixed using neutrophil-derived data.
Fig. 4:
Clustering performance of gene expression data from studies of sepsis, derived from different blood tissue types.Gene expression profiles from patients with sepsis as well as controls were clustered using a partitioning around medoids (PAM) algorithm, with the number of clusters set to 2. Strength of clustering shown is based on the average silhouette width, which ranges from −1 (poor clustering) to 1 (compact and well-separated clusters). Difference between whole blood and isolated leukocyte fractions was significant (P < 0.05).

Owing to the large proportion of studies included in our analysis that focused on pediatric sepsis, we further investigated the influence of age group on both the supervised (sepsis classification) and unsupervised tasks. We found that with data from all cell types combined, specificity for the diagnosis of sepsis was higher in studies of pediatric cohorts than adult cohorts (96% vs 82%, P < 0.05). Average silhouette widths were also higher among pediatric studies (median values 0.33 vs 0.22, P < 0.0001).

An analysis of diagnostic performance and clustering strength limited to the 11 adult studies alone revealed similar findings to the analysis of all 22 studies combined; however, the difference in specificity between whole blood and leukocyte isolate-derived expression data did not reach statistical significance (89% vs 75%, P = 0.34). Average silhouette widths were not significantly different between the two tissue sources (median values 0.21 for whole blood vs 0.16 for isolates, P = 0.13). The lack of statistical significance in these analyses may in part be due to a loss of statistical power in this smaller subset of studies.


We used a structured bioinformatic approach to examine the use of different blood cell compartments in the study of gene expression in sepsis. Gene expression profiling of patients with sepsis has to date used a number of different blood cell types as sources of RNA, but few studies have objectively compared these various approaches. Our study indicates that whole blood is at least as accurate as isolated circulating leukocytes in demonstrating the transcriptional changes that are associated with sepsis. Using whole blood may be a more practical approach to the study of functional genomics in sepsis, as smaller volumes of blood are needed to generate adequate quantities of RNA (31), and samples can be collected directly at the bedside without additional cell separation and purification steps in the laboratory. However, theoretical concerns include the possibility that expression patterns will be heavily influenced by the relative abundance of different leukocyte subtypes within the peripheral circulation at the time of sampling. Statistical methods such as immune cell deconvolution have been used to quantify the contribution of each leukocyte subtype to whole blood gene expression signals in an attempt to account for and mitigate this effect (15), but add additional complexity to bioinformatics workflows.

One study (32) addressed the validity of using whole blood samples directly by comparing the relative expression of different cell type-specific pathways important in the pathophysiology of sepsis. Using gene expression data from whole blood, as well as from lymphocytes, monocytes, and neutrophils in isolation, the authors found that data derived from whole blood showed expression of signature pathways indicative of contributions from all three of these leukocyte subtypes. They also found that the pathways differentially expressed between sepsis patients and healthy controls in the leukocyte subtypes were similarly altered in whole blood samples.

Our study arrived at similar conclusions using a complementary approach based on a systematic analysis of multiple independent gene expression studies. Unlike in previous work, we used expression data directly, without reference to the genes and pathways they represent. This strategy was used to obviate the need for complex methods to merge data from different microarray platforms and study protocols, as well as to explore the utility of signals derived directly from patient samples, with minimal pre-processing and interpretation.

Our results suggest that whole blood-derived gene expression data are able to distinguish patients with sepsis from controls with a high degree of specificity. Whole blood-derived data also formed cohesive clusters, a characteristic of these data that suggests their usefulness in developing genomic classifiers of sepsis, and identifying genomic subtypes of sepsis. By contrast, leukocyte isolate-derived gene expression data tended to be more diffuse, leading to poor cluster differentiation, and less accurate classification of patients.

Leukocyte isolate-derived data showed somewhat mixed results, possibly reflecting the heterogeneity in cell types used in this group. For example, the highest performing datasets were derived from purified monocytes, while poorly performing data were derived from PBMCs that were not further characterized. This pattern is demonstrated by one dataset (GSE 11755) that included gene expression data from different cell types (lymphocytes, monocytes, and whole blood) derived from the same group of pediatric patients with meningococcal sepsis, and non-septic controls (Fig. 5). Data derived from lymphocytes and monocytes formed less cohesive clusters than data derived from whole blood.

Fig. 5:
Principal components analysis for gene expression data derived using lymphocytes, monocytes, and whole blood from the same cohort of pediatric patients with meningococcal sepsis (GSE11755).The shape of the points reflects cluster assignment as determined by partitioning around medoids (PAM) clustering. Expression data from purified cell lines (lymphocytes and monocytes) show greater compactness with overlapping clusters, while whole blood-derived data yield better cluster separation.

While the admixture of various leukocyte types in whole blood has been described as a potential confounder in gene expression studies, our results suggest it may in fact provide an additional layer of information that is useful in identifying and classifying patients with sepsis. For example, measuring gene expression signals from neutrophils in isolation may obscure important differences between patients with higher ratios of neutrophils to PBMCs, and those in whom neutrophil counts are relatively diminished (33). Expression data from purified cell types may therefore be more homogeneous and condensed, with less distance between patients than for data derived from whole blood. One possible explanation for this finding might be changes in gene expression patterns induced by the process of cell subset isolation itself (34). These results support the value of aggregate rather than decomposed biological signals, and suggest the presence of irreducible phenomena that may be obscured when analyzing certain cell types in isolation.

Our study has limitations. First, we included studies from a variety of patient populations, including both adult and pediatric patients, as well as survivors and non-survivors presenting with sepsis, severe sepsis, and septic shock. Differences in gene expression are known to exist among patients with septic shock between the various ages, comorbidities, ethnicities, and severities of illness included in this analysis. Our use of the controls from each study as comparators was intended to address the breadth of sepsis syndromes encountered, reasoning that differences in gene expression would be greater between patients with sepsis and non-sepsis controls, than amid different strata of sepsis patients. We also analyzed studies done among adult populations separately, which revealed a similar pattern of performance characteristics for the diagnosis of sepsis as with all the studies combined, albeit with differences in specificity that were not statistically significant.

Second, our study used gene expression data from a number of different microarray platforms, some of which may be more current, more accurate, and more consistent than others. We addressed this potential confounder by focusing on the expression values themselves rather than gene and pathway annotations, and by analyzing studies using their own internal controls. Additional analyses showed no differences in test performance characteristics between microarray platforms (data not shown). There was, however, a significant difference in cluster cohesiveness between the various microarray platforms used in the included experiments, with Affymetrix experiments yielding the highest average silhouette widths (P < 0.0001, Kruskal–Wallis rank sum test).

Third, while we were able to identify a number of gene expression studies for inclusion, samples sizes were in some cases small (range 9–130 samples, median number of samples = 60). The different leukocyte subtypes examined do share in common a preceding isolation step and a homogenous cellular phenotype. The merging of different leukocyte types done in order to improve sample sizes may, however, have resulted in an averaging of gene expression signals between molecularly distinct cell types (e.g., lymphocytes and neutrophils). Further investigations should focus on determining whether important differences exist in terms of diagnostic test performance and clustering capability, among different types of leukocytes.

Last, the publicly available gene expression data used in our study were annotated by variable and most often minimal amounts of clinical data describing the patients included in the studies. While we were able to demonstrate distinct clusters of patients distinguished by unique gene expression characteristics, we were unable to fully characterize these subgroups on clinical grounds. Our results support the need for future studies to better characterize the clinical phenotypes of genomically distinct sepsis subgroups. Our results also do not preclude the possibility that other approaches – for example expression of cell surface markers or soluble factors – may facilitate the clustering, description, and stratification of patients with sepsis.

In summary, by pooling data from multiple studies of gene expression in sepsis, we found that whole blood provides reliable information, obviating the need for cell isolation and the artifacts that might result. Whole blood gene expression studies may provide reliable information to guide the diagnosis and therapeutic stratification of patients with the complex clinical syndrome of sepsis.


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Critical care; genomics; intensive care; septic shock; severe sepsis; gene expression

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