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.
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.
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.
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.
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