The increasing global burden of sepsis in healthcare calls for better diagnostic tests that allow earlier detection of sepsis and infections that could lead to sepsis. The major problem for patients at risk for sepsis is an immunological imbalance. Cells of the innate immune system, such as monocytes and neutrophils, are the first-line of defence against infections. In the presence of sepsis, these cells produce a flood of inflammatory cytokines, causing widespread inflammation that can lead to death. Monocytes perform multiple immunological functions, and play a role in the development of sepsis-induced inflammation and immunosuppression. Monocyte subpopulations with different functions and morphologies vary in number over the course of the inflammatory response. The monocyte distribution width (MDW) is a novel cellular marker of monocyte anisocytosis that can add significant value to the white blood cell (WBC) count and help detect sepsis in patients entering the emergency department (ED).
by Elena A. Sukhacheva
Sepsis epidemiology and definitions
Sepsis is a major healthcare burden and, despite progress in diagnostic and treatment options, mortality from sepsis remains unacceptably high. The number of septic patients in the U.S., UK and EU is increasing [1–4]. Clearly, there is an unmet need for better diagnostic tests that can provide both the early detection of sepsis and the detection of severe infections that may progress to sepsis, if not diagnosed early enough. Global increases in sepsis frequency may be related to the aging population, as the incidence of sepsis is disproportionately increased in elderly adults, and age is an independent predictor of mortality [5]. Furthermore, immunosuppressive drugs, which are increasingly being used for diverse conditions, may result in more severe infections and increased sepsis frequency [6].
The definition of sepsis has recently been changed from the previous Sepsis-2 definition of a systemic inflammatory response (SIRS) in the presence of an infection [7], to the current Sepsis-3 definition of a life-threatening organ dysfunction caused by a dysregulated host response to infection [8]. The new Sepsis-3 definition reflects newfound understanding that the immune response in sepsis is more complex than previously thought, comprising both pro- and anti-inflammatory mechanisms.
Immune response in sepsis
It is now clear that the major problem for patients with sepsis, or at high risk of developing sepsis, is immunological imbalance, and dysregulation of the mechanisms of innate and adaptive immunity. Sepsis occurs when the immune system begins, in one way or another, to lose the battle against severe infection. After sepsis onset, the production of pro-inflammatory cytokines (IL-1β, IL-6, and tumour necrosis factor [TNFα]) by the cells of the innate immune system (neutrophils and monocytes) may result in a “cytokine storm” that produces overwhelming inflammation, which can lead to blood pressure collapse, coagulation abnormalities and, ultimately, organ failure and death. In the later stages of disease, patients who survive the cytokine storm may die from sepsis-related immunosuppression and an inability of the immune system to combat infection efficiently [9]. Inflammatory and immunosuppressive processes may overlap in sepsis [10,11], further complicating the biology of this fatal condition whose mechanisms are still poorly understood by scientists. Figure 1 shows the current understanding of immune imbalance in sepsis [12]. While all immune cells are involved in the immune response in sepsis [13–16] (Figure 2), this document is mainly focused on changes in monocytes, with other cell populations discussed only briefly.
Under normal conditions, neutrophils usually stay in the circulation for only a few hours and undergo apoptosis within 24 hours of release from the bone marrow. In sepsis, the delay in neutrophil apoptosis [17,18], combined with the increased neutrophil production in the bone marrow, results in neutrophilia. The function of these neutrophils, however, is impaired [19], with decreased chemotactic activity [20,21], decreased antibacterial function and increased production of anti-inflammatory cytokine interleukin 10 (IL-10) [22].
Sepsis also has a profound effect on all the main lymphocyte subpopulations [14]: CD4+ T-cells, CD8+ T-cells and B-cells undergo increased apoptosis; T-regulatory cells are more resistant to sepsis-induced apoptosis, leading to an increased proportion of T-regulatory cells and an immunosupressive phenotype. T-helper cell polarization from a pro-inflammatory Th1 phenotype towards an anti-inflammatory Th2 phenotype also contributes to increased immunosuppression in sepsis.
Monocytes also undergo multiple changes in sepsis, but before discussing these phenomena, it is important to discuss some basic information about the biology and classification of monocytes.
Monocytes’ biology and classification
Monocytes are cells of the innate immune system, the body’s first-line of defence against infection. Other cells of this system include neutrophils, basophils, eosinophils, mast cells, as well as certain types of lymphocytes such as γδ-T-cells and natural killer cells. The innate immune response develops during the first hours and days after pathogen invasion, and the majority of pathogens entering the human body usually are inactivated by this response and do not require adaptive mechanisms with lymphocyte involvement.
Myeloid precursors in the bone marrow differentiate into promonocytes and then into mature monocytes that enter the peripheral blood. These monocytes stay in the circulation for one to three days, after which they migrate into tissues and organs, where they turn into macrophages and dendritic cells. Morphologically, monocytes are large cells measuring 10 to 18 µm in diameter, with convoluted nuclei and azurophilic granules in their cytoplasm.
Monocytes and dendritic cells perform multiple immunological functions that include phagocytosis, antigen presentation and cytokine production. The function of these cells is regulated by a number of cell surface receptors:
- CD14, the receptor for complexes of bacterial lipopolysaccharides and human serum proteins
- Receptors such as CD163 that scavenge membrane fragments and other components of damaged cells
- Multiple receptors for the Fc regions of IgG: CD64 (FcγR1, high-affinity receptor), CD32 (FcγR2, medium-affinity receptor) and CD16 (FcγR3, present only on subpopulations of so-called pro-inflammatory monocytes)
- Other receptors necessary for interaction with lymphocytes and receptors for cytokines
Three subpopulations of monocytes have been characterized in peripheral blood [23–25]. Classical monocytes make up the main monocyte population. Expressing high level CD14 and no CD16 (CD14++CD16-), they represent 80–90% of monocytes in peripheral blood. “Intermediate” monocytes expressing CD16 (CD14++CD16+) are normally found at low numbers, but increase with cytokine stimulation and inflammation. Nonclassical monocytes display decreased expression of CD14 and increased expression of CD16 (CD14+CD16++), and comprise 9%+/-5% of all monocytes, with an average count in healthy donors of approximately 45+/-22 cells/µL [26].
In the literature, nonclassical monocytes are sometimes referred to as inflammatory or pro-inflammatory monocytes; however, published recommendations for the nomenclature of monocytes and dendritic cells in the blood clearly advocate avoiding functional terminology, “because this leads to confusion as the label ‘inflammatory’ has been used for different subpopulations in humans and mice [24].” Also, “these terms may prematurely ascribe functional attributes to cells based on ex vivo studies while they largely remain to be functionally characterized in vivo [24].” Subsets of nonclassical monocytes are expanded dramatically in several pathological conditions including sepsis [26–28], HIV-1 infection [29–33], diabetes [34–35], tuberculosis [36] and other disease states [37].
The recent detailed analysis performed by Mukherjee et al. [28] revealed the functions of monocyte subsets as follows: classical monocytes are phagocytic with no inflammatory attributes, nonclassical subtypes display inflammatory characteristics on activation and display properties for antigen presentation, and intermediate subtypes appear to have both phagocytic and inflammatory functions [28]. In 2017, research based on single-cell RNA sequencing discovered even more subtypes, describing six subpopulations of dendritic cells and four monocyte subpopulations [39]. This classification was based solely on transcriptional activity, and further studies will be needed to understand function and describe the phenotype of all cell subpopulations. Nonetheless, it is clear that morphologically similar cells that we call monocytes may actually have very different functions in human immunity.
Monocytes in sepsis
Monocytes, as cells of first-line defence against infection, are involved in the immune response from very early stages. Abundant literature exists on monocytes and the changes they undergo in sepsis.
A recent study on the dynamics of monocyte subpopulations in peripheral blood at the onset of infection has demonstrated a decrease in the number of peripheral blood monocytes during the early stages of lipopolysaccharide (LPS)-induced acute inflammation in humans. This loss may be due to the migration of monocytes from the blood into tissues, where they differentiate into macrophages and dendritic cells, or it may reflect an increase in monocytes residing in the marginal pool or rolling on the vessel walls [40]. For all three subpopulations of monocytes, the number of cells was decreased at one to two hours after LPS injection. This decrease was followed by a return to the baseline count, but with differences in timing for the three monocyte subsets. This difference in timing means that the early stages of infection, before the appearance of any clinical symptoms, are characterized by differences in the proportions of monocyte subpopulations relative to baseline pre-infection proportions.
Functional changes in monocytes and, in parallel, changes in their cellular morphology, have been demonstrated in the past for a human THP-1 monocytic cell line infected with viable C. pneumonia bacteria [41]. The differentiation of infected cells into macrophages was accompanied by a change to an ameboidor diffused morphology as assessed by microscopy after Giemsa staining.
Multiple studies have demonstrated the importance of HLA-DR expression on monocytes as a prognostic marker in septic patients. A decreased level of HLA-DR expression on monocytes has been found to be a negative prognostic indicator [42–44] and may be used to evaluate the functional activity of the immune system [45,46]. Decreased HLA-DR, as a marker of monocyte anergy, correlates with decreased antigen presentation capacity and decreased pro-inflammatory cytokine release. This has been analyzed mainly by flow cytometry, but, recently, new methods based on real-time PCR have emerged [47,48].
Another monocyte marker, CD16, plays an important role in orchestrating the response of monocytes to Gram-negative sepsis. It has been demonstrated that CD16 on human monocytes is a key regulator of the TRIF-dependent TLR4 signalling pathway, and this pathway is preferentially activated in the CD16+ monocyte subset [49]. Recent publications suggest the variability of monocyte properties in sepsis. Detailed analysis of gene expression in patient monocytes during sepsis and after recovery demonstrated plasticity of monocytes in the course of disease [50]. The significant up-regulation of pro-inflammatory cytokines (IL-1b, IL-6) and chemokines (CCL3 and CCL5) has been demonstrated in sepsis monocytes compared to monocytes after recovery. Transcriptional factor NF-kB, a central transcriptional regulator of the inflammatory response, was also activated in sepsis monocytes, supporting their involvement in severe inflammation. At the same time, anti-inflammatory cytokine IL-10 was found to be up-regulated in sepsis monocytes. These studies once again highlight the diversity of monocytes’ function in sepsis pathogenesis, and their key role in disease progression, with the possible polarization from a pro-inflammatory state to an immunosuppressive state.
More recently, Crouser et al. demonstrated that the morphological variability that occurs during monocyte activation in the early inflammatory response can be captured by measuring the monocyte distribution width (MDW), an indicator of monocyte anisocytosis. Investigators showed that MDW could be a novel cellular marker that may help detect sepsis early in patients admitted to the emergency department (ED) [51]. Multiple morphometric characteristics of monocytes were obtained using a DxH 800 cellular analysis system, which employs physical measurement of cell volume, conductivity and multiple angles of laser scatter to classify leukocytes into five sub-populations and detect the presence of abnormal cells. This study showed that anisocytosis of circulating monocytes provides significant added value to WBC count for the detection of sepsis in the ED population.
Conclusion
In summary, monocytes are a very heterogeneous population of cells that differ in phenotype, size, nuclear morphology, gene profile and function [52]. In sepsis, this diversity is even more pronounced due to functional changes of monocyte subsets, and is accompanied by a variation in monocyte morphology.
Morphological variability is just the tip of the iceberg of the underlying biological heterogeneity, and may be an important early marker of sepsis or severe infections with a high risk of progressing to sepsis. A recent publication from Crouser [51], together with previous research on sepsis using cellular morphometric parameters gathered using a DxH 800 analyser [53–56], may build the foundation for practical usage of MDW in combination with currently-used sepsis markers (WBC, PCT, CRP, IL-6) for early sepsis screening and diagnosis, leading to early initiation of appropriate therapy.
References
1. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. “Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.” Crit Care Med, 2001, vol. 29, no.7, pp. 1303–1310.
2. Brun-Buisson C, Meshaka P, Pinton P, Vallet B. “EPISEPSIS Study Group. EPISEPSIS: a reappraisal of the epidemiology and outcome of severe sepsis in French intensive care units.” Intensive Care Med, 2004, vol. 30, pp. 580–588.
3. van Gestel A, Bakker J, Veraart CP, van Hout BA. “Prevalence and incidence of severe sepsis in Dutch intensive care units.” Crit Care, 2004, vol. 8, pp. R153–62.
4. Harrison DA, Welch CA, Eddleston JM. “The epidemiology of severe sepsis in England, Wales and Northern Ireland, 1996 to 2004: secondary analysis of a high quality clinical database, the ICNARC Case Mix Programme Database.” Crit Care, 2006, vol. 10, p. R42.
5. Martin GSM, Mannino DM, Moss M. “The effect of age on the development and outcome of adult sepsis.” Crit Care Med, 2006, vol. 34, no.1, pp. 15–21.
6. Gea-Banacloche JC, Opal SM, Jorgensen J, Carcillo JA, Sepkowitz KA, Cordonnier C. “Sepsis associated with immunosuppressive medications: an evidence-based review.” Crit Care Med, 2004, vol. 32, no. 11 (suppl.), pp. S578–90.
7. Bone RC, et al. “Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine.” Chest, 1992, vol. 101, pp.1644–55.
8. Singer M, et al. “The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).” JAMA, 2016, vol. 315, no. 8, pp.801–810.
9. Hotchkiss RS, Monneret G, Payen D. “Immunosuppression in sepsis: a novel understanding of the disorder and a new therapeutic approach.” Lancet Infect Di,. 2013, vol. 13, no. 3, pp. 260–268.
10. Adib-Conquy M, Cavaillon JM. “Compensatory anti-inflammatory response syndrome.” Thromb Haemost, 2009, vol. 101, pp. 36–47.
11. Gomez HG, Gonzalez SM, Londoño JM, Hoyos NA, Niño CD, Leon AL, Velilla PA, Rugeles MT, Jaimes FA. “Immunological characterization of compensatory anti-inflammatory response syndrome in patients with severe sepsis: a longitudinal study.” Crit Care Med, 2014, vol. 42, no 4, pp.771–80.
12. Delano MJ, Ward PA. “Sepsis-induced immune dysfunction: can immune therapies reduce mortality?” J Clin Invest, 2016, vol. 126, no. 1, pp. 23–31.
13. Bosmann M. and Ward PA. “The inflammatory response in sepsis.” Trends Immunol, 2013, vol. 34, no. 3, pp. 129–136.
14. Hotchkiss RS, Monneret G, Payen D. “Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy.” Nat Rev Immunol, 2013, vol. 13, no. 12, pp. 862–874.
15. Van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG. “The immunopathology of sepsis and potential therapeutic targets.” Nat Rev Immunol, 2017, vol. 17, pp. 407–420.
16. Stearns-Kurosawa DJ, Osuchowski MF, Valentine C, Kurosawa S, Remick DG. “The pathogenesis of sepsis.” Annu Rev Pathol, 2011, vol. 6, pp. 19–48.
17. Paunel-Görgülü A, Kirichevska T, Lögters T, Windolf J, Flohé S. “Molecular mechanisms underlying delayed apoptosis in neutrophils from multiple trauma patients with and without sepsis.” Mol Med, 2012 vol. 18, pp. 325–335.
18. Tamayo E, Gómez E, Bustamante J, Gómez-Herreras JI, Fonteriz R, Bobillo F, Bermejo-Martín JF, Castrodeza J, Heredia M, Fierro I, Álvarez FJJ “Evolution of neutrophil apoptosis in septic shock survivors and nonsurvivors.” Crit Care, 2012 vol. 27, no. 4, pp. 415.e1–11.
19. Alves-Filho JC, Spiller F, Cunha FQ. “Neutrophil paralysis in sepsis.” Shock, 2010, vol. 34, Suppl 1, pp. 15–21.
20. Kovach MA, Standiford TJ. “The function of neutrophils in sepsis.” Curr Opin Infect Dis. 2012, vol. 25, pp. 321–327.
21. Cummings CJ, et al. “Expression and function of the chemokine receptors CXCR1 and CXCR2 in sepsis.” J Immunol, 1999, vol. 162, pp. 2341–6.
22. Kasten KR, Muenzer JT, Caldwell CC. “Neutrophils are significant producers of IL-10 during sepsis.” Biochem Biophys Res Commun, 2010, vol. 393, pp. 28–31.
23. B Passlick, D Flieger, HW Ziegler-Heitbrock. “Identification and characterization of a novel monocyte subpopulation in human peripheral blood.” Blood, 1989, vol. 74, pp. 2527–2534.
24. Ziegler-Heitbrock L, Ancuta P, Crowe S, Dalod M, Grau V, Hart DN, Leenen PJ, Liu YJ, MacPherson G, Randolph GJ, Scherberich J, Schmitz J, Shortman K, Sozzani S, Strobl H, Zembala M, Austyn JM, Lutz MB. “Nomenclature of monocytes and dendritic cells in blood.” Blood, 2010 vol. 116, no. 16, e74–80.
25. Guilliams M, Ginhoux F, Jakubzick C, Naik SH, Onai N, Schraml BU, Segura E, Tussiwand R, Yona S. “Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny.” Nat Rev Immunol, 2014, vol. 14, no. 8, pp. 571–578.
26. Fingerle G, Pforte A, Passlick B, Blumenstein M, Strobel M, Ziegler-Heitbrock HWL. “The novel subset of CD14+/CD16+ blood monocytes is expanded in sepsis patients.” Blood, 1993, vol. 82, pp. 3170–3176.
27. Skrzeczynska, J, Kobylarz K, Hartwich Z,Zembala M, Pryjma J. “CD14+ CD16+ monocytes in the course of sepsis in neonates and small children: monitoring and functional studies.” Scandinavian J Immun, 2002, vol. 55, no. 6, pp. 629–638.
28. Mukherjee R, Barman PK, Thatoi PK, Tripathy R, Das BK, Ravindran B. “Non-classical monocytes display inflammatory features: validation in sepsis and systemic lupus erythematous.” Scientific Reports, 2015, vol. 5:13886 | DOI: 10.1038/srep13886.
29. Funderburg NT, Zidar DA, Shive C, Lioi A, Mudd J, Musselwhite LW, Simon DI, Costa MA, Rodriguez B, Sieg SF, Lederman MM. “Shared monocyte subset phenotypes in HIV-1 infection and in uninfected subjects with acute coronary syndrome.” Blood, 2012, vol. 120, no. 23, pp. 599–608.
30. Chen P, Su B, Zhang T, Zhu X, Xia W, Fu Y, Zhao G, Xia H, Dai L, Sun L, Liu L, Wu H. “Perturbations of monocyte subsets and their association with T helper cell differentiation in acute and chronic HIV-1¬infected patients.” Front Immunol, 2017, vol. 8, p. 272.
31. Williams DW, Calderon TM, Lopez L, Carvallo-Torres L, Gaskill PJ, Eugenin EA, Morgello S, Berman JW. “Mechanisms of HIV entry into the CNS: increased sensitivity of HIV infected CD14+CD16+ monocytes to CCL2 and key roles of CCR2, JAM-A, and ALCAM in diapedesis.” PLoS One, 2013, vol. 8, no 7:e69270.
32. Ansari AW, Meyer-Olson D, Schmidt RE. “Selective expansion of pro-inflammatory chemokine CCL2¬loaded CD14+CD16+ monocytes subset in HIV-infected therapy naïve individuals.” J Clin Immunol, 2013, vol. 33, no. 1, pp. 302–306.
33. Dutertre CA, Amraoui S, DeRosa A, Jourdain JP, Vimeux L, Goguet M, Degrelle S, Feuillet V, Liovat AS, Müller-Trutwin M, Decroix N, Deveau C, Meyer L, Goujard C, Loulergue P, Launay O, Richard Y, Hosmalin A. “Pivotal role of M-DC8 monocytes from viremic HIV-infected patients in TNFα overproduction in response to microbial products.” Blood, 2012, vol. 120, no. 11, pp. 2259–68.
34. Min D, Brooks B, Wong J, Salomon R, Bao W, Harrisberg B, Twigg SM, Yue DK, McLennan SV. “Alterations in monocyte CD16 in association with diabetes complications.” Mediators Inflamm, 2012; vol. 2012, Article ID 649083.
35. Ryba-Stanisławowska M, Myśliwska J, Juhas U, Myśliwiec M. “Elevated levels of peripheral blood CD14(bright) CD16+ and CD14(dim) CD16+ monocytes may contribute to the development of retinopathy in patients with juvenile onset type 1 diabetes.” APMIS, 2015, vol. 123, no. 9, pp. 793–9.
36. Lugo-Villarino G, Neyrolles O. “Dressed not to kill: CD16+ monocytes impair immune defence against tuberculosis.” Eur J Immunol, 2013, vol. 43, no. 2, pp. 327–30.
37. Fingerle-Rowson G, Auers J, Kreuzer E, Fraunberger P, Blumenstein M, Ziegler-Heitbrock LH. “Expansion of CD14+CD16+ monocytes in critically ill cardiac surgery patients.” Inflammation, 1998, vol. 22, pp. 367–79.
38. Lee J, Tam H, Adler L, Ilstad-Minnihan A, Macaubas C, Mellins ED. “The MHC class II antigen presentation pathway in human monocytes differs by subset and is regulated by cytokines.” PLoS One, 2017, vol. 12, no. 8, e0183594.
39. Villani AC, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, Griesbeck M, Butler A, Zheng S, Lazo S, Jardine L, Dixon D, Stephenson E, Nilsson E, Grundberg I, McDonald D, Filby A, Li W, De Jager PL, Rozenblatt-Rosen O, Lane AA, Haniffa M, Regev A, Hacohen N. “Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors.” Science, 2017, vol. 356, issue 6335, eaah4573.
40. Tak T, van Groenendael R, Pickkers P, Koenderman L. “Monocyte subsets are differentially lost from the circulation during acute inflammation induced by human experimental endotoxemia.” J Innate Immun, 2017, vol. 12, no. 9, pp. 464–74.
41. Yamaguchi Y, Haranaga S, Widen R, Friedman H, Yamamoto Y. “Chlamydia pneumoniae infection induces differentiation of monocytes into macrophages.” Infection and Immunity, 2002, vol. 70, pp. 2392–8.
42. Strohmeyer JC, Blume C, Meisel C, Doecke WD, Hummel M, Hoeflich C, Thiele K, Unbehaun A, Hetzer R, Volk HD. “Standardized immune monitoring for the prediction of infections after cardiopulmonary bypass surgery in risk patients.” Cytometry B Clin Cytom, 2003, vol. 53, no. 1, pp. 54–62.
43. Genel F, Atlihan F, Ozsu E, Ozbek E. “Monocyte HLA-DR expression as predictor of poor outcome in neonates with late onset neonatal sepsis.” J Infect, 2010, vol. 60, no. 3, pp. 224–228.
44. Satoh A, Miura T, Satoh K, Masamune A, Yamagiwa T, Sakai Y, Shibuya K, Takeda K, Kaku M, Shimosegawa T. “Human leukocyte antigen-DR expression on peripheral monocytes as a predictive marker of sepsis during acute pancreatitis.” Pancreas, 2002, vol. 25, no. 3, pp. 245–250.
45. Volk HD, Reinke P, Döcke WD. “Immunological monitoring of the inflammatory process: Which variables? When to assess?” Eur J Surg Suppl, 1999, vol. 584, pp. 70–72.
46. Winkler MS, Rissiek A, Priefler M, Schwedhelm E, Robbe L, Bauer A, Zahrte C, Zoellner C, Kluge S, Nierhaus A. “Human leucocyte antigen (HLA-DR) gene expression is reduced in sepsis and correlates with impaired TNFα response: A diagnostic tool for immunosuppression?” PLoS One, 2017, vol. 12, no. 8, p. e0182427.
47. Cajander S, Bäckman A, Tina E, Strålin K, Söderquist B, Källman J. “Preliminary results in quantitation of HLA-DRA by real-time PCR: a promising approach to identify immunosuppression in sepsis.” Crit Care, 2013, vol. 17, p. R223.
48. Monneret G, Venet F. “Monocyte HLA-DR in sepsis: shall we stop following the flow?” Crit Care, 2014, vol. 18:102.
49. Shalova IN, Kajiji T, Lim JY, Gomez-Pina V, Fernandez-Ruiz I, Arnalich F et al. “CD16 regulates TRIF-dependent TLR4 response in human monocytes and their subsets.” J Immunol, 2012, vol. 188, pp. 3,584–3,593.
50. Shalova IN, Lim JY, Chittezhath M, Zinkernagel AS, Beasley F, Hernandez-Jimenez E, Toledano V, Cubillos-Zapata C, Rapisarda A, Chen J, Duan K, Yang H, Poidinger M, Melillo G, Nizet V, Arnalich F, Lopez-Collazo E, Biswas SK. “Human monocytes undergo functional re-programming during sepsis mediated by hypoxia-inducible factor-1a.” Immunity, 2015, vol. 42, pp. 484–498.
51. Crouser ED, Parrillo JE, Seymour C, Angus DC, Bicking K, Tejidor L, Magari R, Careaga D, Williams J, Closser DR, Samoszuk M, Herren L, Robart E, Chaves F. “Improved early detection of sepsis in the ED with a novel monocyte distribution width biomarker.” Chest, 2017 vol. 152, no. 3, pp. 518–526.
52. Yona S, Jung S. “Monocytes: subsets, origins, fates and functions” Curr Opinion in Hematology, 2010, vol. 17, pp.53–59.
53. Abiramalatha T, Santhanam S, Mammen JJ, Rebekah G, Shabeer MP, Choudhury J, Nair SC. “Utility of neutrophil volume conductivity scatter (VCS) parameter changes as sepsis screen in neonates.” J Perinatol, 2016, vol. 36, no. 9, pp. 733–738.
54. Lee A-J, Kim S-G. “Mean cell volumes of neutrophils and monocytes are promising markers of sepsis in elderly patients.” Blood Research, 2013, vol. 48, no. 3, pp. 193–197.
55. Park D-H, Park K, Park J, Park H-H, Chae H, Lim J, Oh E-J, Kim Y, Park YJ, Han K. “Screening of sepsis using leukocyte cell population data from the Coulter automatic blood cell analyzer DxH800.” Int Jnl Lab Hem, 2011, vol. 33, pp. 391–399.
56. Dilmoula A, Kassengera Z, Turkan H, Dalcomune D, Sukhachev D, Vincent JL, and Pradier O. “Volume, conductivity and scatter properties of leukocytes (VCS technology) in detecting sepsis in critically ill adult patients.” Blood (ASH Annual Meeting Abstracts), 2011, vol. 118, abstract 4729.
The author
Elena A. Sukhacheva, Ph.D.
Senior Manager,
Global Scientific Affairs, Hematology, Beckman Coulter Diagnostics,
Miami, FL, USA
Biomarkers for the diagnosis of sepsis
, /in Featured Articles /by 3wmediaSepsis is a medical emergency that needs rapid identification and treatment to create the best possible outcomes. However, in the early stages it can be very difficult to distinguish sepsis from uncomplicated infection. This article summarizes recent developments in sepsis nomenclature and definitions as well as providing an insight into the role that biomarkers might play in diagnosis and prognosis.
Background
Sepsis is a life-threatening condition associated with high morbidity and mortality, with the risk of death ranging from 30% to 80% depending on the severity of the disease. The World Health Organization estimates that more than 30 million people are affected by sepsis worldwide every year [1], although for reasons discussed by Candel et al., the actual epidemiology of sepsis is difficult to ascertain [2]. In the UK and USA it is thought that sepsis is the cause of around 37 000 and nearly 270 000 deaths per year, respectively [3, 4]. Outcomes of sepsis are better if it is detected and treated early, but despite the large numbers of people affected by it, public awareness of it is still low. In recent years, awareness campaigns have been launched and this year several popular TV and radio programmes in the UK have featured sepsis storylines (Call the Midwife, Coronation Street and The Archers).
Definitions
The difficulties experienced in studying the epidemiology of sepsis are likely to reflect the problems of characterization and diagnosis of the disease, which is in turn a reflection of the complex nature of the condition. Original definitions of sepsis date back to 1991, with the idea that sepsis was caused by systemic inflammatory response syndrome (SIRS) in resulting from infection. In 2001 the definitions were re-examined but left largely unchanged. In 2016, a task force re-evaluated and updated definitions of sepsis and septic shock (Box 1), taking into account improved understanding of the pathobiology of sepsis, which is now recognized to involve early activation of both pro- and anti-inflammatory responses, along with major modifications in non-immunologic pathways such as cardiovascular, neuronal, autonomic, hormonal, bioenergetic, metabolic, and coagulation [5]. A lay definition of sepsis published in 2011 [6] was also accepted by the 2016 task force (Box 1). The definitions created in 1991, 2001 and 2016 have been designated Sepsis-1, Sepsis-2 and Sepsis-3, respectively, to indicate the need for ongoing refinement.
Diagnosis of sepsis
Early diagnosis and treatment of sepsis is associated with improved outcomes, but the difficulty lies in distinguishing sepsis from uncomplicated infection. Identification of patients with sepsis is largely achieved through the use of the Sequential (or Sepsis-Related) Organ Failure Assessment (SOFA) score (Table 1) in the hospital setting or the quick SOFA (qSOFA) score (See Figure 1 “Operationalization of Clinical Criteria Identifying Patients With Sepsis and Septic Shock” in Singer et al. [5]). Commencement of treatment should occur within the first hour of admission and should not be delayed by waiting for results from the lab, as the SOFA score can be applied retrospectively. Management of sepsis also requires (amongst other things) that blood samples are taken before broad spectrum antibiotics are administered and that once the pathogen has been identified antibiotic usage can be refined to aid antimicrobial stewardship (See the Surviving Sepsis Campaign [7] and NICE guidelines [8] for full details of early sepsis management). Sepsis is most commonly caused by bacterial infection, but can also be due to fungal, viral or parasitic infection. However, identification of the pathogen and its antibiotic susceptibility and/or resistance by classic culture techniques is slow and molecular- and proteomic-based approaches, such as matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF) spectroscopy, may improve turnaround times [9].
Biomarkers
The difficulty of distinguishing sepsis from uncomplicated infection has long driven the search for suitable biomarkers to aid sepsis diagnosis. An ideal biomarker would be able to distinguish sepsis from non-infectious causes of critical illness, having a fast and specific increase in sepsis and a rapid decrease after effective therapy. A number of potential biomarkers have been identified, although none are specific enough to be used alone.
Procalcitonin and C-reactive protein
The most-studied biomarkers are procalcitonin and C-reactive protein (CRP). CRP is an acute-phase protein that is secreted from the liver in the response to inflammatory processes and is therefore sensitive but not specific for sepsis. Procalcitonin, again is produced in response to inflammation and infection, and is so far the only biomarker to be used clinically, as it differentiates better than CRP between infectious and non-infectious causes of critical illness. A meta-analysis found that procalcitonin had a mean sensitivity and specificity of around 70% and an area under receiver operator characteristic curve of less than 0.80 [10]. However as levels of procalcitonin are known to be raised after surgery, trauma and viral infection, the Surviving Sepsis Campaign concluded that procalcitonin levels are not adequate to distinguish sepsis from other causes of inflammation [11], although it may be useful for indicating when treatment with antibiotics can end [12].
Interleukin 6 (IL-6)
IL-6 was initially a biomarker of interest for rapid sepsis diagnosis as it has a fast kinetic profile – the concentration increases within 2 hours of onset of sepsis and decreases within 6 hours. However, the results from studies have been mixed, with some suggesting that it was able to discriminate between sepsis and non-infectious illness, whereas others found that procalcitonin was better, hence it has not been added to current guidelines [11].
Promising biomarkers
A number of other biomarkers have been identified that show promise include soluble urokinase-type plasminogen activator receptor, presepsin and proadrenomedullin [2, 13]. Additionally, recently, reduced serum levels of fetuin-A (a major hepatokine) were found to be independently associated with predicting progression to septic shock and higher rates of mortality [14].
Biomarker panels
Even today, no single biomarker has the diagnostic strength to identify patients suffering from sepsis and it is likely that assessing panels of biomarkers will increase the sensitivity and accuracy of diagnosis of sepsis, compared to any individual biomarker (for example, see the study by Kofoed et al. [15]). More recently, the power of mass spectrometry and “-omics studies” is being investigated with some promise, although still suffering from limitations [13].
References
1. Sepsis. World Health Organization 2018; http://www.who.int/news-room/fact-sheets/detail/sepsis.
2. Candel FJ, et al. Current aspects in sepsis approach. Turning things around. Rev Esp Quimioter 2018; 31(4): 298–315.
3. Improving outcomes for patients with sepsis: a cross-system action plan. NHS England 2015; https://www.england.nhs.uk/wp-content/uploads/2015/08/Sepsis-Action-Plan-23.12.15-v1.pdf.
4. Sepsis. Centers for Disease Control and Prevention 2018; https://www.cdc.gov/sepsis/datareports/index.html.
5. Singer M, et al. The Third International Consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315(8): 801–810.
6. Czura CJ. Merinoff symposium 2010: Sepsis – speaking with one voice. Mol Med 2011; 17(1-2): 2–3.
7. Surviving Sepsis Campaign: International guidelines for management of sepsis and septic shock: 2016. Surviving Sepsis Campaign 2016; http://www.survivingsepsis.org/Guidelines/Pages/default.aspx.
8. Sepsis: recognition, diagnosis and early management; NICE guideline [NG51]. National Institutes for Health and Care Excellence 2017; https://www.nice.org.uk/guidance/NG51/chapter/Recommendations#identifying-people-with-suspected-sepsis.
9. Ward KM, Harris R. Sepsis: earlier organism identification using MALDI-TOF. Clin Lab Int 2015; Nov: 14–18.
10. Wacker C, et al. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis 2013; 13: 426–435.
11. Dellinger RP, et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012. Crit Care Med 2013; 41(2): 580–637.
12. Sager R, et al. Procalcitonin-guided diagnosis and antibiotic stewardship revisited. BMC Med 2017; 15: 15.
13. Ludwig KR, Hummon AB. Mass spectrometry for the discovery of biomarkers of sepsis. Mol Biosyst 2017; 13(4): 648–664.
14. Karampela. Karampela I, Kandri E, Antonakos G, Vogiatzakis E, Christodoulatos GS, Nikolaidou A, Dimopoulos G, Armaganidis A, Dalamaga M. Kinetics of circulating fetuin-A may predict mortality independently from adiponectin, high molecular weight adiponectin and prognostic factors in critically ill patients with sepsis: A prospective study. J Crit Care 2017; 41: 78–85.
15. Kofoed K, et al. Use of plasma C-reactive protein, procalcitonin, neutrophils, macrophage migration inhibitory factor, soluble urokinase-type plasminogen activator receptor, and soluble triggering receptor expressed on myeloid cells-1 in combination to diagnose infections: a prospective study. Crit Care 2007; 11(2): R38.
The role of monocytes in the progression of sepsis
, /in Featured Articles /by 3wmediaThe increasing global burden of sepsis in healthcare calls for better diagnostic tests that allow earlier detection of sepsis and infections that could lead to sepsis. The major problem for patients at risk for sepsis is an immunological imbalance. Cells of the innate immune system, such as monocytes and neutrophils, are the first-line of defence against infections. In the presence of sepsis, these cells produce a flood of inflammatory cytokines, causing widespread inflammation that can lead to death. Monocytes perform multiple immunological functions, and play a role in the development of sepsis-induced inflammation and immunosuppression. Monocyte subpopulations with different functions and morphologies vary in number over the course of the inflammatory response. The monocyte distribution width (MDW) is a novel cellular marker of monocyte anisocytosis that can add significant value to the white blood cell (WBC) count and help detect sepsis in patients entering the emergency department (ED).
by Elena A. Sukhacheva
Sepsis epidemiology and definitions
Sepsis is a major healthcare burden and, despite progress in diagnostic and treatment options, mortality from sepsis remains unacceptably high. The number of septic patients in the U.S., UK and EU is increasing [1–4]. Clearly, there is an unmet need for better diagnostic tests that can provide both the early detection of sepsis and the detection of severe infections that may progress to sepsis, if not diagnosed early enough. Global increases in sepsis frequency may be related to the aging population, as the incidence of sepsis is disproportionately increased in elderly adults, and age is an independent predictor of mortality [5]. Furthermore, immunosuppressive drugs, which are increasingly being used for diverse conditions, may result in more severe infections and increased sepsis frequency [6].
The definition of sepsis has recently been changed from the previous Sepsis-2 definition of a systemic inflammatory response (SIRS) in the presence of an infection [7], to the current Sepsis-3 definition of a life-threatening organ dysfunction caused by a dysregulated host response to infection [8]. The new Sepsis-3 definition reflects newfound understanding that the immune response in sepsis is more complex than previously thought, comprising both pro- and anti-inflammatory mechanisms.
Immune response in sepsis
It is now clear that the major problem for patients with sepsis, or at high risk of developing sepsis, is immunological imbalance, and dysregulation of the mechanisms of innate and adaptive immunity. Sepsis occurs when the immune system begins, in one way or another, to lose the battle against severe infection. After sepsis onset, the production of pro-inflammatory cytokines (IL-1β, IL-6, and tumour necrosis factor [TNFα]) by the cells of the innate immune system (neutrophils and monocytes) may result in a “cytokine storm” that produces overwhelming inflammation, which can lead to blood pressure collapse, coagulation abnormalities and, ultimately, organ failure and death. In the later stages of disease, patients who survive the cytokine storm may die from sepsis-related immunosuppression and an inability of the immune system to combat infection efficiently [9]. Inflammatory and immunosuppressive processes may overlap in sepsis [10,11], further complicating the biology of this fatal condition whose mechanisms are still poorly understood by scientists. Figure 1 shows the current understanding of immune imbalance in sepsis [12]. While all immune cells are involved in the immune response in sepsis [13–16] (Figure 2), this document is mainly focused on changes in monocytes, with other cell populations discussed only briefly.
Under normal conditions, neutrophils usually stay in the circulation for only a few hours and undergo apoptosis within 24 hours of release from the bone marrow. In sepsis, the delay in neutrophil apoptosis [17,18], combined with the increased neutrophil production in the bone marrow, results in neutrophilia. The function of these neutrophils, however, is impaired [19], with decreased chemotactic activity [20,21], decreased antibacterial function and increased production of anti-inflammatory cytokine interleukin 10 (IL-10) [22].
Sepsis also has a profound effect on all the main lymphocyte subpopulations [14]: CD4+ T-cells, CD8+ T-cells and B-cells undergo increased apoptosis; T-regulatory cells are more resistant to sepsis-induced apoptosis, leading to an increased proportion of T-regulatory cells and an immunosupressive phenotype. T-helper cell polarization from a pro-inflammatory Th1 phenotype towards an anti-inflammatory Th2 phenotype also contributes to increased immunosuppression in sepsis.
Monocytes also undergo multiple changes in sepsis, but before discussing these phenomena, it is important to discuss some basic information about the biology and classification of monocytes.
Monocytes’ biology and classification
Monocytes are cells of the innate immune system, the body’s first-line of defence against infection. Other cells of this system include neutrophils, basophils, eosinophils, mast cells, as well as certain types of lymphocytes such as γδ-T-cells and natural killer cells. The innate immune response develops during the first hours and days after pathogen invasion, and the majority of pathogens entering the human body usually are inactivated by this response and do not require adaptive mechanisms with lymphocyte involvement.
Myeloid precursors in the bone marrow differentiate into promonocytes and then into mature monocytes that enter the peripheral blood. These monocytes stay in the circulation for one to three days, after which they migrate into tissues and organs, where they turn into macrophages and dendritic cells. Morphologically, monocytes are large cells measuring 10 to 18 µm in diameter, with convoluted nuclei and azurophilic granules in their cytoplasm.
Monocytes and dendritic cells perform multiple immunological functions that include phagocytosis, antigen presentation and cytokine production. The function of these cells is regulated by a number of cell surface receptors:
Three subpopulations of monocytes have been characterized in peripheral blood [23–25]. Classical monocytes make up the main monocyte population. Expressing high level CD14 and no CD16 (CD14++CD16-), they represent 80–90% of monocytes in peripheral blood. “Intermediate” monocytes expressing CD16 (CD14++CD16+) are normally found at low numbers, but increase with cytokine stimulation and inflammation. Nonclassical monocytes display decreased expression of CD14 and increased expression of CD16 (CD14+CD16++), and comprise 9%+/-5% of all monocytes, with an average count in healthy donors of approximately 45+/-22 cells/µL [26].
In the literature, nonclassical monocytes are sometimes referred to as inflammatory or pro-inflammatory monocytes; however, published recommendations for the nomenclature of monocytes and dendritic cells in the blood clearly advocate avoiding functional terminology, “because this leads to confusion as the label ‘inflammatory’ has been used for different subpopulations in humans and mice [24].” Also, “these terms may prematurely ascribe functional attributes to cells based on ex vivo studies while they largely remain to be functionally characterized in vivo [24].” Subsets of nonclassical monocytes are expanded dramatically in several pathological conditions including sepsis [26–28], HIV-1 infection [29–33], diabetes [34–35], tuberculosis [36] and other disease states [37].
The recent detailed analysis performed by Mukherjee et al. [28] revealed the functions of monocyte subsets as follows: classical monocytes are phagocytic with no inflammatory attributes, nonclassical subtypes display inflammatory characteristics on activation and display properties for antigen presentation, and intermediate subtypes appear to have both phagocytic and inflammatory functions [28]. In 2017, research based on single-cell RNA sequencing discovered even more subtypes, describing six subpopulations of dendritic cells and four monocyte subpopulations [39]. This classification was based solely on transcriptional activity, and further studies will be needed to understand function and describe the phenotype of all cell subpopulations. Nonetheless, it is clear that morphologically similar cells that we call monocytes may actually have very different functions in human immunity.
Monocytes in sepsis
Monocytes, as cells of first-line defence against infection, are involved in the immune response from very early stages. Abundant literature exists on monocytes and the changes they undergo in sepsis.
A recent study on the dynamics of monocyte subpopulations in peripheral blood at the onset of infection has demonstrated a decrease in the number of peripheral blood monocytes during the early stages of lipopolysaccharide (LPS)-induced acute inflammation in humans. This loss may be due to the migration of monocytes from the blood into tissues, where they differentiate into macrophages and dendritic cells, or it may reflect an increase in monocytes residing in the marginal pool or rolling on the vessel walls [40]. For all three subpopulations of monocytes, the number of cells was decreased at one to two hours after LPS injection. This decrease was followed by a return to the baseline count, but with differences in timing for the three monocyte subsets. This difference in timing means that the early stages of infection, before the appearance of any clinical symptoms, are characterized by differences in the proportions of monocyte subpopulations relative to baseline pre-infection proportions.
Functional changes in monocytes and, in parallel, changes in their cellular morphology, have been demonstrated in the past for a human THP-1 monocytic cell line infected with viable C. pneumonia bacteria [41]. The differentiation of infected cells into macrophages was accompanied by a change to an ameboidor diffused morphology as assessed by microscopy after Giemsa staining.
Multiple studies have demonstrated the importance of HLA-DR expression on monocytes as a prognostic marker in septic patients. A decreased level of HLA-DR expression on monocytes has been found to be a negative prognostic indicator [42–44] and may be used to evaluate the functional activity of the immune system [45,46]. Decreased HLA-DR, as a marker of monocyte anergy, correlates with decreased antigen presentation capacity and decreased pro-inflammatory cytokine release. This has been analyzed mainly by flow cytometry, but, recently, new methods based on real-time PCR have emerged [47,48].
Another monocyte marker, CD16, plays an important role in orchestrating the response of monocytes to Gram-negative sepsis. It has been demonstrated that CD16 on human monocytes is a key regulator of the TRIF-dependent TLR4 signalling pathway, and this pathway is preferentially activated in the CD16+ monocyte subset [49]. Recent publications suggest the variability of monocyte properties in sepsis. Detailed analysis of gene expression in patient monocytes during sepsis and after recovery demonstrated plasticity of monocytes in the course of disease [50]. The significant up-regulation of pro-inflammatory cytokines (IL-1b, IL-6) and chemokines (CCL3 and CCL5) has been demonstrated in sepsis monocytes compared to monocytes after recovery. Transcriptional factor NF-kB, a central transcriptional regulator of the inflammatory response, was also activated in sepsis monocytes, supporting their involvement in severe inflammation. At the same time, anti-inflammatory cytokine IL-10 was found to be up-regulated in sepsis monocytes. These studies once again highlight the diversity of monocytes’ function in sepsis pathogenesis, and their key role in disease progression, with the possible polarization from a pro-inflammatory state to an immunosuppressive state.
More recently, Crouser et al. demonstrated that the morphological variability that occurs during monocyte activation in the early inflammatory response can be captured by measuring the monocyte distribution width (MDW), an indicator of monocyte anisocytosis. Investigators showed that MDW could be a novel cellular marker that may help detect sepsis early in patients admitted to the emergency department (ED) [51]. Multiple morphometric characteristics of monocytes were obtained using a DxH 800 cellular analysis system, which employs physical measurement of cell volume, conductivity and multiple angles of laser scatter to classify leukocytes into five sub-populations and detect the presence of abnormal cells. This study showed that anisocytosis of circulating monocytes provides significant added value to WBC count for the detection of sepsis in the ED population.
Conclusion
In summary, monocytes are a very heterogeneous population of cells that differ in phenotype, size, nuclear morphology, gene profile and function [52]. In sepsis, this diversity is even more pronounced due to functional changes of monocyte subsets, and is accompanied by a variation in monocyte morphology.
Morphological variability is just the tip of the iceberg of the underlying biological heterogeneity, and may be an important early marker of sepsis or severe infections with a high risk of progressing to sepsis. A recent publication from Crouser [51], together with previous research on sepsis using cellular morphometric parameters gathered using a DxH 800 analyser [53–56], may build the foundation for practical usage of MDW in combination with currently-used sepsis markers (WBC, PCT, CRP, IL-6) for early sepsis screening and diagnosis, leading to early initiation of appropriate therapy.
References
1. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. “Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.” Crit Care Med, 2001, vol. 29, no.7, pp. 1303–1310.
2. Brun-Buisson C, Meshaka P, Pinton P, Vallet B. “EPISEPSIS Study Group. EPISEPSIS: a reappraisal of the epidemiology and outcome of severe sepsis in French intensive care units.” Intensive Care Med, 2004, vol. 30, pp. 580–588.
3. van Gestel A, Bakker J, Veraart CP, van Hout BA. “Prevalence and incidence of severe sepsis in Dutch intensive care units.” Crit Care, 2004, vol. 8, pp. R153–62.
4. Harrison DA, Welch CA, Eddleston JM. “The epidemiology of severe sepsis in England, Wales and Northern Ireland, 1996 to 2004: secondary analysis of a high quality clinical database, the ICNARC Case Mix Programme Database.” Crit Care, 2006, vol. 10, p. R42.
5. Martin GSM, Mannino DM, Moss M. “The effect of age on the development and outcome of adult sepsis.” Crit Care Med, 2006, vol. 34, no.1, pp. 15–21.
6. Gea-Banacloche JC, Opal SM, Jorgensen J, Carcillo JA, Sepkowitz KA, Cordonnier C. “Sepsis associated with immunosuppressive medications: an evidence-based review.” Crit Care Med, 2004, vol. 32, no. 11 (suppl.), pp. S578–90.
7. Bone RC, et al. “Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine.” Chest, 1992, vol. 101, pp.1644–55.
8. Singer M, et al. “The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).” JAMA, 2016, vol. 315, no. 8, pp.801–810.
9. Hotchkiss RS, Monneret G, Payen D. “Immunosuppression in sepsis: a novel understanding of the disorder and a new therapeutic approach.” Lancet Infect Di,. 2013, vol. 13, no. 3, pp. 260–268.
10. Adib-Conquy M, Cavaillon JM. “Compensatory anti-inflammatory response syndrome.” Thromb Haemost, 2009, vol. 101, pp. 36–47.
11. Gomez HG, Gonzalez SM, Londoño JM, Hoyos NA, Niño CD, Leon AL, Velilla PA, Rugeles MT, Jaimes FA. “Immunological characterization of compensatory anti-inflammatory response syndrome in patients with severe sepsis: a longitudinal study.” Crit Care Med, 2014, vol. 42, no 4, pp.771–80.
12. Delano MJ, Ward PA. “Sepsis-induced immune dysfunction: can immune therapies reduce mortality?” J Clin Invest, 2016, vol. 126, no. 1, pp. 23–31.
13. Bosmann M. and Ward PA. “The inflammatory response in sepsis.” Trends Immunol, 2013, vol. 34, no. 3, pp. 129–136.
14. Hotchkiss RS, Monneret G, Payen D. “Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy.” Nat Rev Immunol, 2013, vol. 13, no. 12, pp. 862–874. 15. Van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG. “The immunopathology of sepsis and potential therapeutic targets.” Nat Rev Immunol, 2017, vol. 17, pp. 407–420.
16. Stearns-Kurosawa DJ, Osuchowski MF, Valentine C, Kurosawa S, Remick DG. “The pathogenesis of sepsis.” Annu Rev Pathol, 2011, vol. 6, pp. 19–48.
17. Paunel-Görgülü A, Kirichevska T, Lögters T, Windolf J, Flohé S. “Molecular mechanisms underlying delayed apoptosis in neutrophils from multiple trauma patients with and without sepsis.” Mol Med, 2012 vol. 18, pp. 325–335.
18. Tamayo E, Gómez E, Bustamante J, Gómez-Herreras JI, Fonteriz R, Bobillo F, Bermejo-Martín JF, Castrodeza J, Heredia M, Fierro I, Álvarez FJJ “Evolution of neutrophil apoptosis in septic shock survivors and nonsurvivors.” Crit Care, 2012 vol. 27, no. 4, pp. 415.e1–11.
19. Alves-Filho JC, Spiller F, Cunha FQ. “Neutrophil paralysis in sepsis.” Shock, 2010, vol. 34, Suppl 1, pp. 15–21.
20. Kovach MA, Standiford TJ. “The function of neutrophils in sepsis.” Curr Opin Infect Dis. 2012, vol. 25, pp. 321–327.
21. Cummings CJ, et al. “Expression and function of the chemokine receptors CXCR1 and CXCR2 in sepsis.” J Immunol, 1999, vol. 162, pp. 2341–6.
22. Kasten KR, Muenzer JT, Caldwell CC. “Neutrophils are significant producers of IL-10 during sepsis.” Biochem Biophys Res Commun, 2010, vol. 393, pp. 28–31.
23. B Passlick, D Flieger, HW Ziegler-Heitbrock. “Identification and characterization of a novel monocyte subpopulation in human peripheral blood.” Blood, 1989, vol. 74, pp. 2527–2534.
24. Ziegler-Heitbrock L, Ancuta P, Crowe S, Dalod M, Grau V, Hart DN, Leenen PJ, Liu YJ, MacPherson G, Randolph GJ, Scherberich J, Schmitz J, Shortman K, Sozzani S, Strobl H, Zembala M, Austyn JM, Lutz MB. “Nomenclature of monocytes and dendritic cells in blood.” Blood, 2010 vol. 116, no. 16, e74–80.
25. Guilliams M, Ginhoux F, Jakubzick C, Naik SH, Onai N, Schraml BU, Segura E, Tussiwand R, Yona S. “Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny.” Nat Rev Immunol, 2014, vol. 14, no. 8, pp. 571–578.
26. Fingerle G, Pforte A, Passlick B, Blumenstein M, Strobel M, Ziegler-Heitbrock HWL. “The novel subset of CD14+/CD16+ blood monocytes is expanded in sepsis patients.” Blood, 1993, vol. 82, pp. 3170–3176.
27. Skrzeczynska, J, Kobylarz K, Hartwich Z,Zembala M, Pryjma J. “CD14+ CD16+ monocytes in the course of sepsis in neonates and small children: monitoring and functional studies.” Scandinavian J Immun, 2002, vol. 55, no. 6, pp. 629–638.
28. Mukherjee R, Barman PK, Thatoi PK, Tripathy R, Das BK, Ravindran B. “Non-classical monocytes display inflammatory features: validation in sepsis and systemic lupus erythematous.” Scientific Reports, 2015, vol. 5:13886 | DOI: 10.1038/srep13886.
29. Funderburg NT, Zidar DA, Shive C, Lioi A, Mudd J, Musselwhite LW, Simon DI, Costa MA, Rodriguez B, Sieg SF, Lederman MM. “Shared monocyte subset phenotypes in HIV-1 infection and in uninfected subjects with acute coronary syndrome.” Blood, 2012, vol. 120, no. 23, pp. 599–608.
30. Chen P, Su B, Zhang T, Zhu X, Xia W, Fu Y, Zhao G, Xia H, Dai L, Sun L, Liu L, Wu H. “Perturbations of monocyte subsets and their association with T helper cell differentiation in acute and chronic HIV-1¬infected patients.” Front Immunol, 2017, vol. 8, p. 272.
31. Williams DW, Calderon TM, Lopez L, Carvallo-Torres L, Gaskill PJ, Eugenin EA, Morgello S, Berman JW. “Mechanisms of HIV entry into the CNS: increased sensitivity of HIV infected CD14+CD16+ monocytes to CCL2 and key roles of CCR2, JAM-A, and ALCAM in diapedesis.” PLoS One, 2013, vol. 8, no 7:e69270.
32. Ansari AW, Meyer-Olson D, Schmidt RE. “Selective expansion of pro-inflammatory chemokine CCL2¬loaded CD14+CD16+ monocytes subset in HIV-infected therapy naïve individuals.” J Clin Immunol, 2013, vol. 33, no. 1, pp. 302–306.
33. Dutertre CA, Amraoui S, DeRosa A, Jourdain JP, Vimeux L, Goguet M, Degrelle S, Feuillet V, Liovat AS, Müller-Trutwin M, Decroix N, Deveau C, Meyer L, Goujard C, Loulergue P, Launay O, Richard Y, Hosmalin A. “Pivotal role of M-DC8 monocytes from viremic HIV-infected patients in TNFα overproduction in response to microbial products.” Blood, 2012, vol. 120, no. 11, pp. 2259–68.
34. Min D, Brooks B, Wong J, Salomon R, Bao W, Harrisberg B, Twigg SM, Yue DK, McLennan SV. “Alterations in monocyte CD16 in association with diabetes complications.” Mediators Inflamm, 2012; vol. 2012, Article ID 649083.
35. Ryba-Stanisławowska M, Myśliwska J, Juhas U, Myśliwiec M. “Elevated levels of peripheral blood CD14(bright) CD16+ and CD14(dim) CD16+ monocytes may contribute to the development of retinopathy in patients with juvenile onset type 1 diabetes.” APMIS, 2015, vol. 123, no. 9, pp. 793–9.
36. Lugo-Villarino G, Neyrolles O. “Dressed not to kill: CD16+ monocytes impair immune defence against tuberculosis.” Eur J Immunol, 2013, vol. 43, no. 2, pp. 327–30.
37. Fingerle-Rowson G, Auers J, Kreuzer E, Fraunberger P, Blumenstein M, Ziegler-Heitbrock LH. “Expansion of CD14+CD16+ monocytes in critically ill cardiac surgery patients.” Inflammation, 1998, vol. 22, pp. 367–79.
38. Lee J, Tam H, Adler L, Ilstad-Minnihan A, Macaubas C, Mellins ED. “The MHC class II antigen presentation pathway in human monocytes differs by subset and is regulated by cytokines.” PLoS One, 2017, vol. 12, no. 8, e0183594.
39. Villani AC, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, Griesbeck M, Butler A, Zheng S, Lazo S, Jardine L, Dixon D, Stephenson E, Nilsson E, Grundberg I, McDonald D, Filby A, Li W, De Jager PL, Rozenblatt-Rosen O, Lane AA, Haniffa M, Regev A, Hacohen N. “Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors.” Science, 2017, vol. 356, issue 6335, eaah4573.
40. Tak T, van Groenendael R, Pickkers P, Koenderman L. “Monocyte subsets are differentially lost from the circulation during acute inflammation induced by human experimental endotoxemia.” J Innate Immun, 2017, vol. 12, no. 9, pp. 464–74.
41. Yamaguchi Y, Haranaga S, Widen R, Friedman H, Yamamoto Y. “Chlamydia pneumoniae infection induces differentiation of monocytes into macrophages.” Infection and Immunity, 2002, vol. 70, pp. 2392–8.
42. Strohmeyer JC, Blume C, Meisel C, Doecke WD, Hummel M, Hoeflich C, Thiele K, Unbehaun A, Hetzer R, Volk HD. “Standardized immune monitoring for the prediction of infections after cardiopulmonary bypass surgery in risk patients.” Cytometry B Clin Cytom, 2003, vol. 53, no. 1, pp. 54–62.
43. Genel F, Atlihan F, Ozsu E, Ozbek E. “Monocyte HLA-DR expression as predictor of poor outcome in neonates with late onset neonatal sepsis.” J Infect, 2010, vol. 60, no. 3, pp. 224–228.
44. Satoh A, Miura T, Satoh K, Masamune A, Yamagiwa T, Sakai Y, Shibuya K, Takeda K, Kaku M, Shimosegawa T. “Human leukocyte antigen-DR expression on peripheral monocytes as a predictive marker of sepsis during acute pancreatitis.” Pancreas, 2002, vol. 25, no. 3, pp. 245–250.
45. Volk HD, Reinke P, Döcke WD. “Immunological monitoring of the inflammatory process: Which variables? When to assess?” Eur J Surg Suppl, 1999, vol. 584, pp. 70–72.
46. Winkler MS, Rissiek A, Priefler M, Schwedhelm E, Robbe L, Bauer A, Zahrte C, Zoellner C, Kluge S, Nierhaus A. “Human leucocyte antigen (HLA-DR) gene expression is reduced in sepsis and correlates with impaired TNFα response: A diagnostic tool for immunosuppression?” PLoS One, 2017, vol. 12, no. 8, p. e0182427.
47. Cajander S, Bäckman A, Tina E, Strålin K, Söderquist B, Källman J. “Preliminary results in quantitation of HLA-DRA by real-time PCR: a promising approach to identify immunosuppression in sepsis.” Crit Care, 2013, vol. 17, p. R223.
48. Monneret G, Venet F. “Monocyte HLA-DR in sepsis: shall we stop following the flow?” Crit Care, 2014, vol. 18:102.
49. Shalova IN, Kajiji T, Lim JY, Gomez-Pina V, Fernandez-Ruiz I, Arnalich F et al. “CD16 regulates TRIF-dependent TLR4 response in human monocytes and their subsets.” J Immunol, 2012, vol. 188, pp. 3,584–3,593.
50. Shalova IN, Lim JY, Chittezhath M, Zinkernagel AS, Beasley F, Hernandez-Jimenez E, Toledano V, Cubillos-Zapata C, Rapisarda A, Chen J, Duan K, Yang H, Poidinger M, Melillo G, Nizet V, Arnalich F, Lopez-Collazo E, Biswas SK. “Human monocytes undergo functional re-programming during sepsis mediated by hypoxia-inducible factor-1a.” Immunity, 2015, vol. 42, pp. 484–498.
51. Crouser ED, Parrillo JE, Seymour C, Angus DC, Bicking K, Tejidor L, Magari R, Careaga D, Williams J, Closser DR, Samoszuk M, Herren L, Robart E, Chaves F. “Improved early detection of sepsis in the ED with a novel monocyte distribution width biomarker.” Chest, 2017 vol. 152, no. 3, pp. 518–526.
52. Yona S, Jung S. “Monocytes: subsets, origins, fates and functions” Curr Opinion in Hematology, 2010, vol. 17, pp.53–59.
53. Abiramalatha T, Santhanam S, Mammen JJ, Rebekah G, Shabeer MP, Choudhury J, Nair SC. “Utility of neutrophil volume conductivity scatter (VCS) parameter changes as sepsis screen in neonates.” J Perinatol, 2016, vol. 36, no. 9, pp. 733–738.
54. Lee A-J, Kim S-G. “Mean cell volumes of neutrophils and monocytes are promising markers of sepsis in elderly patients.” Blood Research, 2013, vol. 48, no. 3, pp. 193–197.
55. Park D-H, Park K, Park J, Park H-H, Chae H, Lim J, Oh E-J, Kim Y, Park YJ, Han K. “Screening of sepsis using leukocyte cell population data from the Coulter automatic blood cell analyzer DxH800.” Int Jnl Lab Hem, 2011, vol. 33, pp. 391–399.
56. Dilmoula A, Kassengera Z, Turkan H, Dalcomune D, Sukhachev D, Vincent JL, and Pradier O. “Volume, conductivity and scatter properties of leukocytes (VCS technology) in detecting sepsis in critically ill adult patients.” Blood (ASH Annual Meeting Abstracts), 2011, vol. 118, abstract 4729.
The author
Elena A. Sukhacheva, Ph.D.
Senior Manager,
Global Scientific Affairs, Hematology, Beckman Coulter Diagnostics,
Miami, FL, USA
Literature review: Sepsis
, /in Featured Articles /by 3wmediaExtracellular vesicles as markers and mediators in sepsis
Raeven P, Zipperle J, Drechsler S. Theranostics 2018; 8(12):3348–3365
Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. It remains a highly lethal condition in which current tools for early diagnosis and therapeutic decision-making are far from ideal. Extracellular vesicles (EVs), 30 nm to several micrometers in size, are released from cells upon activation and apoptosis and express membrane epitopes specific for their parental cells. Since their discovery two decades ago, their role as biomarkers and mediators in various diseases has been intensively studied. However, their potential importance in the sepsis syndrome has gained attention only recently. Sepsis and EVs are both complex fields in which standardization has long been overdue. In this review, several topics are discussed. First, we review current studies on EVs in septic patients with emphasis on their variable quality and clinical utility. Second, we discuss the diagnostic and therapeutic potential of EVs as well as their role as facilitators of cell communication via micro RNA and the relevance of microorganism-derived EVs. Third, we give an overview over the potential beneficial but also detrimental roles of EVs in sepsis. Finally, we focus on the role of EVs in selected intensive care scenarios such as coagulopathy, mechanical ventilation and blood transfusion. Overall, the prospect for EV use in septic patients is bright, ranging from rapid and precise (point-of-care) diagnostics, prevention of harmful iatrogenic interventions, to using EVs as guides of individualized therapy. Before the above is achieved, however, the EV research field requires reliable standardization of the current methods and development of new analytical procedures that can close the existing technological gaps.
Diagnostic accuracy of lipopolysaccharide-binding protein for sepsis in patients with suspected infection in the Emergency Department
García de Guadiana Romualdo L, Albaladejo Otón MD, Rebollo Acebes S, Esteban Torrella P, Hernando Holgado A, et al. Ann Clin Biochem 2018; 55(1): 143–148
BACKGROUND: Biomarkers can facilitate the diagnosis of sepsis, enabling early management and improving outcomes. Lipopolysaccharide-binding protein (LBP) has been reported as a biomarker for the detection of infection, but its diagnostic value is controversial. In this study, we assessed the diagnostic accuracy of LBP for sepsis in the Emergency Department (ED) patients, comparing it with more established biomarkers of sepsis, including procalcitonin (PCT) and C-reactive protein (CRP).
METHODS: LBP and other sepsis biomarkers, including PCT and CRP, were measured on admission in 102 adult patients presenting with suspected infection. Classification of patients was performed using the recently updated definition for sepsis (Sepsis-3). The diagnostic accuracy of LBP, CRP and PCT for sepsis was evaluated by using receiver operating characteristic curve (ROC) analysis.
RESULTS: A total of 49 patients were classified as having sepsis. In these patients, median (interquartile range) LBP (41.8 [41.1] µg/dL vs 26.2 [25] µg/dL), CRP (240 [205] mg/L vs 160 [148] mg/dL) and PCT (5.19 [13.68] µg/L vs 0.39 [1.09] µg/L) were significantly higher than in patients classified as not having sepsis (P<0.001 for all three biomarkers). ROC curve analysis and area under curve (AUC) revealed a value of 0.701 for LBP, similar to CRP (0.707) and lower than that for PCT (0.844) (P=0.012).
CONCLUSION: In adult ED patients with suspected infection, the diagnostic accuracy for sepsis of LBP is similar to that of CRP but lower than that of PCT.
An innovative approach for the integration of proteomics and metabolomics data in severe septic shock patients stratified for mortality
Cambiaghi A, Díaz R, Martinez JB, Odena A, Brunelli L, et al. Sci Rep 2018; 8(1):6681
In this work, we examined plasma metabolome, proteome and clinical features in patients with severe septic shock enrolled in the multicentre ALBIOS study. The objective was to identify changes in the levels of metabolites involved in septic shock progression and to integrate this information with the variation occurring in proteins and clinical data. Mass spectrometry-based targeted metabolomics and untargeted proteomics allowed us to quantify absolute metabolite concentration and relative protein abundance. We computed the ratio D7/D1 to take into account their variation from day 1 (D1) to day 7 (D7) after shock diagnosis. Patients were divided into two groups according to 28-day mortality. Three different elastic net logistic regression models were built: one on metabolites only, one on metabolites and proteins and one to integrate metabolomics and proteomics data with clinical parameters. Linear discriminant analysis and partial least squares discriminant analysis were also implemented. All the obtained models correctly classified the observations in the testing set. By looking at the variable importance (VIP) and the selected features, the integration of metabolomics with proteomics data showed the importance of circulating lipids and coagulation cascade in septic shock progression, thus capturing a further layer of biological information complementary to metabolomics information.
Validity of biomarkers in screening for neonatal sepsis – a single-center hospital-based study
Rashwan NI, Hassan MH, Mohey El-Deen ZM, Ahmed AE. Pediatr Neonatol 2018; doi: 10.1016/j.pedneo.2018.05.001 [Epub ahead of print]
BACKGROUND: The diagnosis of neonatal sepsis still considered to be a challenge for both clinicians and the laboratory owing to the non-specific clinical presentations. The present study aimed to compare and assess the diagnostic and prognostic values of C-reactive protein (CRP), high sensitivity CRP (hsCRP), presepsin, interleukin-6 (IL-6) and procalcitonin (PCT) in neonatal sepsis separately and in combination.
METHODS: This hospital-based cross-sectional study has been conducted on 168 neonates recruited from the Neonatal Intensive Care Unit of Qena University Hospitals, Upper Egypt. Measurements of CRP using the latex agglutination test, hsCRP, presepsin, IL6 and PCT assays using commercially available ELISA assay kits were done to all included neonates.
RESULTS: There were significantly higher serum levels of CRP among late onset versus early onset sepsis group with significantly higher serum levels of hsCRP and presepsin among early onset compared with the late onset sepsis group (P<0.05 for all). There were significantly higher hsCRP, presepsin and PCT serum levels in proven versus probable sepsis group (P<0.05 for all). Significantly higher serum levels of presepsin and PCT were noted among survivors versus non-survivors sepsis group (P<0.05 for all). The cut-off value of the serum level of CRP >6 mg/dL showed lower sensitivity and specificity than that of hsCRP at cut-off >140 ng/mL in diagnosing neonatal sepsis. The cut-off value of presepsin >200 ng/mL showed equal sensitivity and specificity to IL-6 at cut-off >22 pg/mL. The cut-off value of PCT at >389 pg/mL showed sensitivity and specificity approximate to that of hsCRP.
CONCLUSIONS: CRP could be a helpful prognostic marker in late onset neonatal sepsis. hsCRP and PCT have higher diagnostic accuracy in neonatal sepsis in comparison to other studied markers. Both IL-6 and presepsin have equal diagnostic utility in neonatal sepsis, but presepsin could be a helpful diagnostic marker in early onset neonatal sepsis.
Plasma miRNA-223 correlates with risk, inflammatory markers as well as prognosis in sepsis patients
Wu X, Yang J, Yu L, Long D. Medicine 2018; 97(27):e11352
The purpose was to evaluate the role of plasma microRNA-223 (miRNA-223) in risk and prognosis in sepsis patients, and its correlation with inflammatory markers. In this study, 187 sepsis patients from July 2015 to December 2016 were consecutively enrolled. Blood samples from septic patients and healthy controls (HCs) were collected, and plasma was separated for miRNA-223 expression detected by quantitative real-time PCR (qPCR). Enzyme-linked immune sorbent assay (ELISA) was performed to detect inflammatory markers. The results were as follows: miRNA-223 was highly expressed in sepsis patients compared to HCs (P<0.001). Receiver operating characteristic (ROC) curve revealed miRNA-223 disclosed a good diagnostic value of sepsis with area under curve (AUC) of 0.754, 95 % CI: 0.706–0.803. Sensitivity and specificity were 56.6 % and 86.6 % at the best cut-off point, respectively. Multivariate logistic analysis indicated that miRNA-223 could predict sepsis risk independently. Spearman’s correlation disclosed that miRNA-223 relative expression positively correlated with APCHE II score (r=0.459, P<0.001), CRP (r=0.326, P<0.001), TNFα (r=0.325, P<0.001), IL-1β (r=0.165, P=0.024), IL-6 (r=0.229, P=0.002) and IL-8 (r=0.154, P=0.035), while it negatively correlated with IL-10 (r=−0.289, P<0.001). miRNA-223 expression in non-survivors was higher than that in survivors (P<0.001). ROC curve revealed miRNA-223 could distinguish sepsis non-survivors from survivors with AUC of 0.600, 95 % CI: 0.505–0.695. Sensitivity and specificity were 83.5 % and 38.9 % respectively at the best cut-off point. In conclusion, plasma miRNA-223 correlates with disease severity and inflammatory markers levels, and it might serve as a novel diagnostic and prognostic biomarker in sepsis patients.
Biomarkers of endothelial dysfunction predict sepsis mortality in young infants: a matched case-control study
Wright JK, Hayford K, Tran V, Al Kibria GM, Baqui A, et al. BMC Pediatr 2018; 18(1):118
BACKGROUND: Reducing death due to neonatal sepsis is a global health priority, although there are limited tools to facilitate early recognition and treatment. We hypothesized that measuring circulating biomarkers of endothelial function and integrity (i.e. Angiopoietin-Tie2 axis) would identify young infants with sepsis and predict their clinical outcome.
METHODS: We conducted a matched case-control (1:3) study of 98 young infants aged 0–59 days of life presenting to a referral hospital in Bangladesh with suspected sepsis. Plasma levels of Ang-1, Ang-2, sICAM-1, and sVCAM-1 concentrations were measured at admission. The primary outcome was mortality (n=18); the secondary outcome was bacteremia (n=10).
RESULTS: Ang-2 concentrations at presentation were higher among infants who subsequently died of sepsis compared to survivors (aOR 2.50, P=0.024). Compared to surviving control infants, the Ang-2:Ang-1 ratio was higher among infants who died (aOR 2.29, P=0.016) and in infants with bacteremia (aOR 5.72, P=0.041), and there was an increased odds of death across Ang-2:Ang-1 ratio tertiles (aOR 4.82, P=0.013).
CONCLUSIONS: This study provides new evidence linking the Angiopoietin-Tie2 pathway with mortality and bacteremia in young infants with suspected sepsis. If validated in additional studies, markers of the angiopoietin-Tie2 axis may have clinical utility in risk stratification of infants with suspected sepsis.
Development and first evaluation of a novel multiplex real-time PCR on whole blood samples for rapid pathogen identification in critically ill patients with sepsis
van de Groep K, Bos MP, Savelkoul PHM, Rubenjan A, Gazenbeek C, et al. Eur J Clin Microbiol Infect Dis 2018; 37(7):1333–1344
Molecular tests may enable early adjustment of antimicrobial therapy and be complementary to blood culture (BC) which has imperfect sensitivity in critically ill patients. We evaluated a novel multiplex real-time PCR assay to diagnose bloodstream pathogens directly in whole blood samples (BSI-PCR). BSI-PCR included 11 species- and four genus-specific PCRs, a molecular Gram-stain PCR, and two antibiotic resistance markers. We collected 5 mL blood from critically ill patients simultaneously with clinically indicated BC. Microbial DNA was isolated using the Polaris method followed by automated DNA extraction. Sensitivity and specificity were calculated using BC as reference. BSI-PCR was evaluated in 347 BC-positive samples (representing up to 50 instances of each pathogen covered by the test) and 200 BC-negative samples. Bacterial species-specific PCR sensitivities ranged from 65 to 100 %. Sensitivity was 26 % for the Gram-positive PCR, 32 % for the Gram-negative PCR, and ranged 0 to 7 % for yeast PCRs. Yeast detection was improved to 40 % in a smaller set-up. There was no overall association between BSI-PCR sensitivity and time-to-positivity of BC (which was highly variable), yet Ct-values were lower for true-positive versus false-positive PCR results. False-positive results were observed in 84 (4 %) of the 2200 species-specific PCRs in 200 culture-negative samples, and ranged from 0 to 6 % for generic PCRs. Sensitivity of BSI-PCR was promising for individual bacterial pathogens, but still insufficient for yeasts and generic PCRs. Further development of BSI-PCR will focus on improving sensitivity by increasing input volumes and on subsequent implementation as a
bedside test.
From traditional biochemical signals to molecular markers for detection of sepsis after burn injuries
Muñoz B, Suárez-Sánchez R, Hernández-Hernández O, Franco-Cendejas R, Cortés H, Magaña JJ. Burns 2018; doi: 10.1016/j.burns.2018.04.016 [Epub ahead of print]
Sepsis is a life-threatening organ-dysfunction condition caused by a dysregulated response to an infectious condition that can cause complications in patients with major trauma. Burns are one of the most destructive forms of trauma; despite the improvements in medical care, infections remain an important cause of burn injury-related mortality and morbidity, and complicated sepsis predisposes patients to diverse complications such as organ failure, lengthening of hospital stays, and increased costs. Accurate diagnosis and early treatment of sepsis may have a beneficial impact on clinical outcome of burn-injured patients. In this review, we offer a comprehensive description of the current and traditional markers used as indicative of sepsis in burned patients. However, although these are markers of the inflammatory post-burn response, they usually fail to predict sepsis in severely burned patients because they do not reflect the severity of the infection. Identification and measurement of biomarkers in early stages of infection is important in order to provide a timely response and the effective treatment of burned patients. Therefore, we compiled important experimental evidence, demonstrating novel biomarkers, including molecular markers such as genomic DNA variations, alterations of transcriptome profiling (mRNA, miRNAs, lncRNAs and circRNAs), epigenetic markers, and advances in proteomics and metabolomics. Finally, this review summarizes next-generation technologies for the identification of markers for detection of sepsis after burn injuries.
RESIST-5 O.O.K.N.V.
, /in Featured Articles /by 3wmediaValidated antibodies and antigens for in vitro diagnostics
, /in Featured Articles /by 3wmediaELISA for antibody diagnostics in infectious diseases
, /in Featured Articles /by 3wmediaDrug testing, fingerprints and the future
, /in Featured Articles /by 3wmediaStandard drug testing is regularly carried out using urine, blood or oral fluid. However, fingerprints present a good alternative, as the sample collection is non-invasive, rapid and safe. Herein, we describe the application of two different testing methods for the detection of cocaine in fingerprint samples.
by Dr Catia Costa, Dr Mahado Ismail and Dr Melanie J. Bailey
Drug abuse in the United Kingdom is on the rise and it is a cause for concern, with widespread financial and social implications [1, 2]. The ever-growing drug and alcohol culture in the UK has led to the implementation of workplace drug testing in many industries, especially those in high-risk operational environments. Consequently, there has been a surge in the demand for drug-screening suppliers to develop faster and more reliable testing. This demand is set to increase the market value of drug and alcohol testing in the UK from £167 million to £231 million by 2019 [3].
Conventionally, drug testing is carried out using biological matrices such as blood, urine and, more recently, oral fluid. These matrices and methods of analysis, although established, present a few problems relating to sample collection and transportation. The collection of blood requires medically trained personnel and sample collection is considered invasive, whereas urine carries privacy concerns. Oral fluid is an alternative matrix used for non-invasive drug testing, although sample collection can be time-consuming. All these three matrices are also biohazardous, making sample storage and transportation a potential issue. The potential use of fingerprints for drug testing has become the subject of many recent publications. Fingerprint samples present a good alternative for drug testing as collection is non-invasive and rapid, and there are no known biohazards associated with the sample. Additionally, the fingerprint pattern can be used for donor identification.
Chemical analysis of fingerprints
The chemical information embedded in a fingerprint sample has been reviewed by many, and several publications have explored the detection of substances such as cocaine [4–6], heroin [7], methadone [8], lorazepam [9], methamphetamines [10], caffeine [11] and cough medicine [12] in fingerprints after administration of the substances. These reports are predominantly based on liquid chromatography-mass spectrometry (LC-MS), which is very well established in the field of toxicology for its quantitative potential as well as its sensitivity and reliability. New advances in the field of mass spectrometry saw the rise of ambient ionization mass spectrometry techniques that allow the sample to be analysed in its native state, under ambient conditions. Examples include desorption electrospray ionization (DESI), liquid extraction surface analysis (LESA) and paper spray, which have been applied to the detection of cocaine and metabolites in fingerprint samples [4–6].
Most of these reports in the literature have looked at fingerprint samples collected after administration of the substances. However, no research has investigated the significance of the detection of these substances compared to a large background population of non-drug users. This is of particular importance as a positive test result may be the outcome of contamination by contact with contaminated surfaces or handling the parent drug rather than ingestion. This directly highlights the need for a sampling strategy that removes any contact residue while providing enough fingerprint material for the analysis.
Detection of cocaine in fingerprints
The detection of cocaine in fingerprints has been studied and reported by Ismail et al. [7]. This study looked at fingerprints collected from the background population (i.e. non-drug users) and from patients at a drug rehabilitation clinic. Both sets of samples were collected as presented and after handwashing, followed by wearing nitrile gloves for 10 minutes. Fingerprint results were supported by oral fluid analysis and patient testimony. Analysis of samples collected from patients (n=13) at the rehabilitation clinic yielded a 100% detection rate for cocaine for samples collected as presented and after handwashing. However, the detection of the cocaine metabolite, benzoylecgonine (BZE), decreased from 94% from samples collected as presented, to 87% for samples collected after handwashing. To evaluate the significance of the results above, fingerprint samples collected from the background population were analysed to investigate the prevalence of these substances in non-drug users. Samples collected as presented (n=99 samples) returned a 13% and 5% detection rate for cocaine and BZE, respectively. After handwashing, cocaine was only detected in 1% of the samples analysed (n=100) and no BZE was present. These findings suggest that cocaine can be detected in the background population owing to environmental exposure (e.g. contact with bank notes). However, after using a handwashing procedure, cocaine and benzoylecgonine were not prevalent. Collection of fingerprint samples after a hand-cleaning procedure is therefore advantageous to reduce potential false-positive rates that can be observed from environmental exposure.
As previously mentioned, the use of chromatographic methods is well established in the field of toxicology. However, such methods often rely on extensive sample preparation and analysis. To overcome this issue we have developed paper spray-mass spectrometry (PS-MS) for the detection of cocaine in under 4 minutes from fingerprints collected from patients seeking treatment at a rehabilitation centre [5]. For this method fingerprints are collected on a triangular piece of paper, which is in turn placed on the paper spray source for analysis. An internal standard, solvent and voltage are applied to the paper, resulting in the extraction and ionization of the fingerprint residues before detection on the mass spectrometer (Fig. 1). The method was evaluated with 239 fingerprint samples collected from drug users at the National Health Service (NHS) rehabilitation clinics and from the background population. A positive result was based on the detection of cocaine or one of its two main metabolites, BZE and ecgonine methyl ester (EME). A 99% true-positive rate was achieved on the samples collected from patients at drug rehabilitation centres, which was supported by standard saliva drug testing and patient testimony. Analysis of samples collected from the general population yielded a 2.5% false-positive rate. This follows from the work by Ismail et al. [7] described above, where in the absence of a hand-cleaning procedure cocaine was detected in the background population. Both studies highlight the need for a well-defined sample collection procedure to eliminate false-positive results while maintaining true-positives.
This method has since its publication been shortened to 30 seconds and it has also been applied to the detection of heroin, morphine, codeine, 6-AM and explosive materials. This highlights the potential for the technique to be on a par with current testing methods that target a wide range of substances.
Fingerprint visualization
Another advantage of using fingerprints for drug testing is the possibility to integrate a fingerprint visualization step for donor identification. This would be of particular benefit for preventing cheating and also in cases of disputed results where one would be able to prove that the results were derived from the correct person. Silver nitrate was used to visualize fingerprint samples collected from drug users by treating the substrate before sample collection. Upon collection, samples were exposed to ultraviolet light to bring out the fingerprint pattern (Fig. 2). Analysis of fingerprint samples collected from drug users after silver nitrate development yielded a 100% detection rate for cocaine, showing great potential for this development step to be included in the fingerprint testing routine.
The future: treatment adherence monitoring
Treatment non-adherence is a well-known problem in the NHS and it is estimated that it can cost over £500 million each year [13]. Thus, the establishment of an adherence monitoring tool could result in substantial savings for the NHS. Fingerprint testing offers the opportunity for remote testing where the samples can be collected by the patient at home and sent to the laboratory for analysis. In cases of non-adherence, medical professionals may intervene and ensure the patient is receiving adequate treatment. This is of particular interest for conditions known to have poor adherence rates such as diabetes, cardiovascular diseases and mental health disorders [14] or for highly infectious diseases such as tuberculosis.
References
1. Barber S, Harker R, Pratt A. Human and financial costs of drug addiction. House of Commons Library 2017.
2. Health matters: preventing drug misuse deaths (GOV.CO.UK2017). Public Health England 2017 (https: //www.gov.uk/government/publications/health-matters-preventing-drug-misuse-deaths/health-matters-preventing-drug-misuse-deaths).
3. Eurofins Workplace Drug Testing launches new holistic ‘wrap around service’ to assist UK plc. Eurofins 2018 (https: //www.eurofins.co.uk/forensic-services/press-releases/uk-growing-drug-culture/).
4. Bailey MJ, Bradshaw R, Francese S, Salter TL, Costa C, Ismail M, Webb RP, Bosman I, Wolff K, de Puit M. Rapid detection of cocaine, benzoylecgonine and methylecgonine in fingerprints using surface mass spectrometry. Analyst 2015; 140(18): 6254–629.
5. Costa C, Webb R, Palitsin V, Ismail M, de Puit M, Atkinson S, Bailey MJ. Rapid, secure drug testing using fingerprint development and paper spray mass spectrometry. Clin Chem 2017; 63(11): 1745–17525.
6. Bailey MJ, Randall EC, Costa C, Salter TL, Race AM, de Puit M, Koeberg M, Baumert M, Bunch J. Analysis of urine, oral fluid and fingerprints by liquid extraction surface analysis coupled to high resolution MS and MS/MS – opportunities for forensic and biomedical science. Anal Methods 2016; 8(16): 3373–3382.
7. Ismail M, Stevenson D, Costa C, Webb R, de Puit M, Bailey M. Noninvasive detection of cocaine and heroin use with single fingerprints: determination of an environmental cutoff. Clin Chem 2018; 64(6): 909–917.
8. Jacob S, Jickells S, Wolff K, Smith N. Drug testing by chemical analysis of fingerprint deposits from methadone-maintained opioid dependent patients using UPLC-MS/MS. Drug Metab Lett 2008; 2(4): 245–247.
9. Goucher E, Kicman A, Smith N, Jickells S. The detection and quantification of lorazepam and its 3-O-glucuronide in fingerprint deposits by LC-MS/MS. J Sep Sci 2009; 32(13): 2266–2272.
10. Zhang T, Chen X, Yang R, Xu Y. Detection of methamphetamine and its main metabolite in fingermarks by liquid chromatography-mass spectrometry. Forensic Sci Int 2015; 248: 10–14.
11. Kuwayama K, Tsujikawa K, Miyaguchi H, Kanamori T, Iwata YT, Inoue H. Time-course measurements of caffeine and its metabolites extracted from fingertips after coffee intake: a preliminary study for the detection of drugs from fingerprints. Anal Bioanal Chem 2013; 405(12): 3945–3952.
12. Kuwayama K, Yamamuro T, Tsujikawa K, Miyaguchi H, Kanamori T, Iwata YT, Inoue H. Time-course measurements of drugs and metabolites transferred from fingertips after drug administration: usefulness of fingerprints for drug testing. Forensic Toxicol 2014: 32(2): 235–242.
13. Trueman P, Taylor D, Lowson K, Bligh A, Meszaros A, Wright D, Glanville J, Newbould J, Bury M, et al. Evaluation of the scale, causes and costs of waste medicines. York Health Economics Consortium/School of Pharmacy, University of London 2010.
14. Cutler RL, Fernandez-Llimos F, Frommer M, Benrimoj C, Garcia-Cardenas V. Economic impact of medication non-adherence by disease groups: a systematic review. BMJ Open 2018; 8(1): e016982.
The authors
Catia Costa*1 PhD, Mahado Ismail2 PhD and Melanie J. Bailey2 PhD
1Ion Beam Centre, University of Surrey, Surrey, GU2 7XH, UK
2Department of Chemistry, University of Surrey, Surrey, GU2 7XH, UK
*Corresponding author
E-mail: c.d.costa@surrey.ac.uk
5th volume in Stago’s “Practical Manual in Hemostasis” collection
, /in Featured Articles /by 3wmediaThe last two volumes were devoted to anticoagulants (parenteral, then oral). Now the Clinical Development Department has turned its attention to the exploration of bleeding disorders.
The aim of this series launched in 2014 with the objective of publishing one volume each year, is to provide health professionals with clear and comprehensive medical and scientific information relating to their everyday practice in the wide field of hemostasis. Each issue brings together a panel of international experts.
This latest volume devoted to bleeding disorders (BD) addresses all aspects of this complex clinical situation. The diagnosis of inherited BD, either the most prevalent such as von Willebrand disease, hemophilia A and B or other rare clotting factor defects, is often frightening for patients and their families and constitutes challenging situations for the clinician. Advances in laboratory and pharmaceutical technology have led to an exciting time in the management of people with these diseases, with the potential to significantly improve safety, notably in the perioperative and prophylactic treatment settings, at the same time improving long-term outcomes and quality of life. Nine renowned international authors from Europe and North America were involved in the compilation of this book, coordinated by Stago.
Presented and distributed at the last Congress of the International Society of Thrombosis and Hemostasis (ISTH SSC 2018 Meeting – Dublin) in July, this 5th opus was extremely well received and all 350 copies available on the Stago booth had gone in just 3 days!
Mainly intended for clinicians and pathologists, but also for students and care providers interested in advances in the field of hemostasis and thrombosis, the 5 volumes in the series – of which more than 25,000 copies in all have already been distributed – are available on request to Stago.
Practical Manual series – Format A5 – in English
www.stago.com
New generation cellular analysis line
, /in Featured Articles /by 3wmediaRookie of the year – MALDI-8020 is the newcomer
, /in Featured Articles /by 3wmedia