Leukocyte adhesive function in Intensive Care patients with non-infectious and infectious systemic in ammation

Anqi Li StickyCell Pty LTd Angela Jacques StickyCell Pyt Ltd Bárbara de la Peña Avalos StickyCell Pyt Ltd Yih Rue Ong StickyCell Pty Ltd Kirstin Elgass StickyCell Pty Ltd Emma Saylor Princess Alexandra Hospital Jason Meyer Princess Alexandra Hospital James Walsham Princess Alexandra Hospital Kerina Denny Royal Brisbane and Women's Hospital Jeffery Lipman The University of Queensland Qiang Cheng (  colin@stickycell.com.au ) StickyCell Pty Ltd, Brisbane https://orcid.org/0000-0001-8730-1620 Peter Kruger Princess Alexandra Hospital


Introduction
Dysregulated host response, previously known as Systemic Inflammatory Response Syndrome (SIRS), is a complex inflammatory systemic response to non-infectious or infectious foreign insults 1,2 . Non-infectious causes include major surgery, trauma, burns and severe tissue injury, whereas infectious causes (also known as sepsis) include bacterial, fungal or viral infection [3][4][5] . All causes can result in similar clinical manifestations with the descriptors recently being redefined with the SEPSIS-3 criteria 1 . Due to different aetiology, the treatments for non-infectious and infectious causes of systemic inflammation differ dramatically.
Antibiotic therapy and source control are the first line of treatment for sepsis, whilst antibiotic therapy is not indicated for non-infectious causes. For many patients it is extremely difficult to rapidly and accurately determine disease pathogenesis and the current practice is that patients will be given antibiotics when potential infection is suspected, frequently leading to overuse of antibiotics for patients with non-infectious causes. Antibiotics are continued until the treating clinician is satisfied that the cause of the systemic inflammation is not infection. This overuse of antibiotics in patients with non-infectious causes could result in the emergence of resistant pathogens, as well as other avoidable side effects. It is critical to develop new technology to aid the early differentiation between non-infectious and infectious inflammation. This will allow more targeted therapies to be applied earlier, avoiding unnecessary treatments and may improve survival rates 3 .
In order to differentiate sepsis from non-infectious inflammation, most work has focussed on the determination and characterisation of potential pathogens in patient blood and/or tissues 4 .
Currently, blood cultures or other clinically relevant positive microbiology is the gold standard for the identification of infection in ICU patients with systemic inflammation. A significant proportion of patients with infection will have negative cultures 5 . The turn-around time for microbiological culture results is usually 12-72 hours. Earlier administration of antibiotics has been associated with improved survival and the "time critical" nature of both recognition and treatment of patients with sepsis is increasingly emphasised 1,6,7 .
Additionally, even though a number of molecular tools have been developed to characterise infectious pathogens, the lack of rapidity and sensitivity is a common drawback of these techniques 8 . Biomarkers of host immune response, such as procalcitonin (PCT) and Creactive protein (CRP), have been investigated to separate infectious and non-infectious inflammation. Unfortunately, PCT tests lack the negative predictive power to withhold antibiotics in critically ill patients 9 , suggesting the clinical applications of these biomarkers remain to be better defined [10][11][12] . Thus, there is urgent clinical demand for new sepsis diagnostic tests, particularly that specifically reflect the patient immune activation during inflammation.
The recruitment of leukocytes from the circulation to the surrounding tissues is a hallmark of immune system activation during inflammation 13,14 . To be recruited, circulating leukocytes must undergo a cascade of interactions with blood vessel endothelial cell (EC) surface and subsequently transmigrate into the tissue. Thus, leukocyte adhesive function is defined as the leukocyte ability to interact with and transmigrate through the ECs. Initially, the fast travelling leukocyte will tether and roll on the ECs, which requires the interaction between leukocytes expressing P-selectin glycoprotein ligand-1 (PSGL-1) and endothelial P or E selectins 15 . The rolling leukocytes will then be activated by chemokines and reduce their speed. This allows the interaction between leukocyte 41 integrins with their endothelial ligands, such as vascular cell adhesion molecule-1 (VCAM-1), leading to leukocyte firm adhesion on endothelial surface 16,17 . The adherent leukocytes then crawl along the endothelial surface until they find an optimal spot to leave the vasculature 13,18 . Thus, the activity of leukocyte PSGL-1 and 41 integrin plays a central role in the regulation of leukocyte adhesive function, which will then determine the leukocytes potential to transmigrate and cause inflammation. Additionally, the leukocyte expressing chemokine receptors may facilitate the leukocyte recruitment process via chemotaxis.
Given the divergent causes of inflammation, it was hypothesised that leukocyte adhesive functions could be altered differently in non-infectious and infectious patients. In the present study, a newly developed blood test platform, the leukocyte adhesive function assay (LAFA), was used to identify such differences. By analysing blood samples from ICU patients with non-infectious or infectious systemic inflammation using LAFA, we aimed to compare the ability of specific leukocyte subsets to interact or respond to given endothelial adhesive substrates, including P+E selectin or VCAM-1 in the presence and absence of IL-8, and thereby the ability of LAFA to distinguish non-infectious and infectious patients may be evaluated.

Patient recruitment
This study was approved by Metro South Human Research Ethics Committee, Brisbane Australia (Reference number: 17/QPATH/571). Patients were recruited for this study by screening patients who were admitted to the Intensive Care Unit (ICU), at Princess Alexandra Hospital, Brisbane Australia. Eligible patients were over 18-years of age with a newly identified systemic inflammatory response who could provide prior consent or had a surrogate who could provide prior consent. Patients who had pre-existing inflammatory conditions, such as multiple sclerosis, Crohn's disease, colitis, arthritis, lupus, etc. or were currently on anti-adhesion therapy, including Natalizumab, Vedolizumab or an anti-adhesion clinical trial were excluded from the study. All patients enrolled had any two or more of the following four criteria for systemic inflammation, regardless of the causes of inflammation 19 : 1. Body temperature >38°C or <36°C 2. Heart rate >90 per minute 3. Respiratory rate >20 breaths per minute or PaCO2 <32mmHg. 4. White blood cell count >12,000/mm 3 or <4,000/mm 3 or >10% bands.
The blood samples were collected within 48 hours after the first identification of the systemic inflammatory response. Whole blood was collected in a Heparin blood collection tubes (5ml) for LAFA assays and an EDTA tube (4ml) for full blood exam (FBE). A full blood cell exam was performed using a Mindray BC5000 Haematology Analyser according to manufacturer's instructions.

Sepsis Adjudication
Two clinicians (JW / PK) provided a retrospective and independent assessment of all recruited patients. They were blinded to LAFA results but assessed patients' medical records and laboratory results to determine either definite "infection", definite "no infection" or "possible infection". Concordance between the two assessors was required for a patient to be allocated to the definite infection or no infection group. Where the assessors differed (on 3 occasions) patients were categorised as having possible infection. Fluorescence microscopy time series were recorded on an InCell Analyser 2200 (GE HealthCare, Seattle, WA) with a 10X objective. All data acquisition was recorded at 1 frame per two second for 10 minutes, at the centre of the channel (approximately 9mm from the channel inlet). All experiments were performed in a 37ºC temperature controlled environment.

Cell tracking and data analysis
Cell tracking was accomplished using TrackMate from Fiji image analysis software. Cells were tracked automatically by detecting quality-filtered fluorescent spots in each frame and then linked with a maximum distance of 75µm and maximum gap size of 2. All tracks were subsequently checked manually and corrected for errors. Cell kinetic parameters, including cell density, speed, diffusion coefficient, straightness, dwell time and track length were determined for each interacting cell. Each parameter characterises cell migratory behaviours from one specific aspect. These cell kinetic parameters and their descriptions are list in Table 1. Table 1 A list of cell kinetic parameters with their definition that were used to characterise migratory behaviours.

Cell kinetic parameters Descriptions
Cell density The number of interacting cells detected as valid interactions (cells appear in more than 3 consecutive frames).

Normalised cell density
The correspondent cell counts were used to normalise the cell density results to reduce the effects from variable cell counts between individual blood donors 20 .
R-factor R-factor is calculated as (% of interacting cell type) / (% cell type in circulation) 21 .

Speed
The distant over time of a cell. The speed values of all detected cells were averaged for each blood sample. The lower the cell speed, the less the cell mobility, usually indicating stronger cell and substrate interaction.

Diffusion coefficient
The diffusion coefficient is calculated as mean square displacement divided by 4 times the time the cell travelled 22 . A high diffusion coefficient value usually indicates a clear direction of cell movement. On the contrast, a low diffusion coefficient value suggests a non-directional cell movement, usually indicating strong cell and substrate binding.

Straightness
The ratio of displacement (the direct distance between two points) over track length. The straightness values of all detected cells were averaged for each blood sample. Low cell straightness value usually indicates high level of random cell migration which is independent from the blood flow, a consequence of strong cell and substrate interaction.

Dwell time
The total duration of a cell appearing in the video. The dwell time values of all detected cells were averaged for each blood sample. A high value of cell dwell time usually indicates a strong cell and substrate interaction.

Track length
The total length of a cell travelled. The track length values of all detected cells were averaged for each blood sample. The lower the cell track length, the less the cell mobility, usually indicating stronger cell and substrate interaction.
If a cell appeared in at least 3 consecutive frames, this interaction was defined as a valid interaction and this cell was included in the downstream data analysis, in which all cell kinetic parameters were then determined for this interacting cell. The values of each cell kinetic parameter from each valid cell were averaged within each leukocyte subset (e.g. CD4, CD8, CD15+CD16+ cells, etc) and presented as an averaged value for this subset.

Statistics
Data is reported as median (inter-quartile range) or mean (Standard deviation) as appropriate for its distribution. Appropriate parametric or non-parametric tests were used and a value of p < 0.05 was considered significant. The results for the infection, no infection and possible infection groups are reported, with statistical comparisons made between the infection and no infection patients.

Demographics and stratification of ICU patients
A total of 28 patients were recruited to this study. Clinicians JW and PK reviewed the clinical records of each patient and independently adjudicated whether the patient had infection, no infection or were 'indeterminate/possible infection'. This assessment was done independent of the LAFA results and the adjudication was not released until all LAFA assays were completed.
As a result, 10 patients were deemed to have infection, 9 no infection and the remaining 9 possible infection. The baseline demographics for the ICU patients are presented in Table 2. The clinical diagnosis and potential causes of systemic inflammation in each ICU patient are listed in Table 3.

Migratory behaviours of leukocytes from patients with infectious and non-infectious inflammation.
Leucocyte behaviour across the study groups is shown in Figure 1A to 1H, using a number of cell kinetic parameters. When analysed on P+E selectin substrates, the straightness of interacting CD4 cells in infectious patients was significantly lower than non-infectious cells All blood samples were also analysed on VCAM-1+IL-8 substrates. As shown in Figure 3C and Our study extends the results of previous work that described an enhanced neutrophil recruitment by VCAM-1 in patients with infection, but not in patients with non-infectious systemic inflammation 21 . In our study, interacting leukocytes were recorded during the entire period of flow experiments utilising recently developed LAFA technology to enable a more complete assessment of leukocyte recruitment and cell migratory behaviours. In contrast to the previous findings, a slightly decreased number of interacting neutrophils (CD15+CD16+ cells) on VCAM-1 substrate was detected in infectious patients when compared to noninfectious patients (Figure 2A and 2B). This difference could be due to the divergent approaches used to detect the interacting cells between the two studies. Our study also noted that the neutrophil mobility was lower in infectious patients than non-infectious patients ( Figure 2H), suggesting that the reduced neutrophil mobility in neutrophils from infectious patients on VCAM-1 substrate may serve as a new marker to distinguish infectious patients from non-infectious patients.
IL-8 is a chemokine that is shown to guide the migration of leukocytes (mainly neutrophils) by forming a concentration gradient, a process known as chemotaxis 25 . CXCR1, a receptor for IL-8, may be expressed on neutrophil cell membranes and plays an important role in the regulation of leukocyte functions and migratory behaviours. Our study analysed blood samples by LAFA using VCAM-1 plus IL-8 as substrates, allowing the assessment of leukocyte CXCR1 activities.
An impaired response to IL-8 induced chemotaxis in neutrophils from patients with infection has been previously reported 26 . Consistently, in the present study, a reduced ability to respond to IL-8 was detected in infectious neutrophils, compared with non-infectious cells ( Figure 3C and 3D). These results suggest that the neutrophil IL-8 receptors (e.g. CXCR1) may play different roles in regulation of inflammation in non-infectious and infectious patients, which may allow it to serve as a useful marker to distinguish these two diseases.
The "Possible Infection (or Unknown)" group provides several unique insights. Firstly, it is likely this group contains both non-infectious and infectious patients, which may explain in part why it is indistinguishable from either Non-infectious or Infectious group in majority of cell parameters when analysed on all adhesive substrate (Figure 1, 2 and 3).
Additionally, patient UN-02 was determined to be "possible infection" by both ICU specialists even though no evidence of infectious pathogen was found (Table 3). Given a low CD4 straightness value (0.595) was detected in this patient, and our notion that CD4 straightness may serve as a biomarker for sepsis, this result would support the cause of inflammation in patient UN-02 is more likely to be infectious than non-infectious.
Despite the small cohort size, this study has provided promising data showing the ability of