Multiparameter Affinity Microchip for Early Sepsis Diagnosis Based on

Subscriber access provided by University | of Minnesota Libraries

Article

A Multi-Parameter Affinity Microchip for Early Sepsis Diagnosis Based on CD64 and CD69 Expression and Cell Capture Ye Zhang, Yun Zhou, Wenjie Li, Veronica J Lyons, Amanda Johnson, Amanda Venable, John Griswold, and Dimitri Pappas Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b05305 • Publication Date (Web): 25 May 2018 Downloaded from http://pubs.acs.org on May 25, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

For submission to: Analytical Chemistry

Revised Manuscript A Multi-Parameter Affinity Microchip for Early Sepsis Diagnosis Based on CD64 and CD69 Expression and Cell Capture

Ye Zhang1, Yun Zhou1, Wenjie Li1, Veronica Lyons1, Amanda Johnson2, Amanda Venable2, John Griswold3, and Dimitri Pappas1,* 1

Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, 2Clinical

Research Institute, Texas Tech Health Sciences Center, Lubbock, TX, and 3Department of Surgery, Texas Tech Health Sciences Center, Lubbock, TX *[email protected]

*Corresponding author

1

ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abstract Sepsis is a leading cause of death worldwide. In this work, a multi-parameter affinity microchip was developed for faster sepsis diagnosis, which can reduce the mortality caused by late validation. The separation device captured cells expressing CD25, CD64, and CD69 into discrete antibody regions. The performance of multi-parameter cell separation microchips was compared with flow cytometry analysis and validated with samples of septic patients (n=15) and healthy volunteers (n=10). The total analysis time was 2 hours. Results showed that total on-chip cell counts for both CD64 and CD69 regions were linear with antigen expression levels. The difference between cell capture for septic and healthy samples was statistically significant (CD64: p=0.0033; CD69: p=0.0221, 95% confidence interval), indicating that sepsis is distinguishable based on microfluidic cell capture. For on-chip detection of CD64+ and CD69+ leukocytes, the AUC was 0.95 and 0.78, respectively. The combination of CD64 and CD69 for sepsis diagnosis had the AUC of 0.98, indicating the improved and excellent diagnostic performance of multiple parameters.

2


Page 2 of 27

Page 3 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Introduction Sepsis is a complicated syndrome caused by the body’s dysregulated response to infections.1 This systemic immune response is shaped by pathogen factors and host factors with characteristics that evolve over time. Sepsis remains a leading cause of death despite improving outcomes in healthcare.2 Treatment of sepsis consumes half of intensive care unit resources.3-5 Even patients who are treated and discharged still bear an under recognized risk of physical and cognitive impairment. These patients have a high chance of readmission and a more-than-doubled risk of dying in the following 5 years.6,7 Diagnosis and monitoring of sepsis are complicated due to the variable and non-specific nature of the signs and symptoms of sepsis.6,8,9 This diagnostic uncertainty may result in delays in the initiation of life saving therapies. In addition, extant diagnostic methods may further increase the overuse of prophylactic antimicrobial agents, leading to increased microbial drug resistance.10-12 Accurate and timely detection of sepsis remains a challenge. The SOFA (Sequential Organ Failure Assessment) score is the criteria for sepsis diagnosis based on vital signs, and organ dysfunction can be represented by a SOFA score of 2 points or more.1 However, some treatments (such as ventilation and vasopressors) may alter vital signs, which impacts the ability to use these clinical measurements to monitor patient response to sepsis treatment after initial diagnosis. In addition, bedside diagnosis is followed by 1-3 days of specimen culture for infectious pathogen identification and 1-2 more days for drug susceptibility testing.13 The whole diagnostic process takes longer than disease progression, resulting in a large diagnostic gap in the treatment pathway.14 As an alternative, biomarkers have been studied and found to be helpful in the early diagnosis of sepsis.14,15 3



Over 200 biomarkers have been reported as indicators related to sepsis, including receptor biomarkers, coagulation biomarkers, acute phase protein biomarkers, and biomarkers related to organ dysfunction, vasodilation, and vascular endothelial damage.14,16,17 C-reactive protein (CRP) is the most widely studied biomarker of sepsis in critically ill patients.17-19 Higher CRP values indicate higher readmission and higher mortality.20 However, CRP is not clinically accepted due to its low sensitivity and specificity. Also, increased CRP values can occur in other inflammation and infectious disorders.21 Procalcitonin (PCT) has been proposed as a better prognostic marker than CRP, but the sensitivity and specificity is still low and it cannot differentiate sepsis from other non-infection caused systemic inflammatory response syndromes.22-24 Neutrophil CD64 expression has shown to be a highly sensitive parameter for sepsis diagnosis and prognosis (97.6% sensitivity, 95.9% specificity, AUC = 0.985).25-30 Rogina et al. concluded that neutrophil CD64 expression was the best biomarker for sepsis diagnosis among candidates including CD64 expression, CRP concentration, PCT concentration, leukocytes counts, and neutrophil percentage.30 Increased number of CD4+CD25+ lymphocytes has also been found in the peripheral blood of septic patients,31,32 and showed potential in predicting sepsis.33 CD69 is another promising cell surface receptor for early sepsis diagnosis. Upregulation of CD69 expression was found both in mice models and septic patients.34-36 Due to the complex processes involved in sepsis, a single biomarker may suffer from false positive results. The combination of biomarkers may therefore provide a more accurate conclusion for sepsis diagnosis.

4


Page 4 of 27

Page 5 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


There is great need for a new diagnostic tool that will allow rapid and effective diagnosis of sepsis. Microfluidic technologies are emerging as powerful tools for point-of-care (POC) testing and liquid biopsies, including sepsis detection.37-40 Kemmler et al.,41 Buchegger et al.,42 and Baldini et al.43, have reported different microarrays for diagnosis of sepsis by testing CRP, IL-6, PCT, or neopterin, respectively. Neutrophil chemotaxis was evaluated as a parameter to diagnose sepsis on a microfluidic chip by Lu et al.44 Microfluidic chips can also be used in pathogen detection from blood samples of sepsis patients. Guan et al.45 reported a chip bacterial analysis. Recently, both the Bashir group38 and our group40 independently developed and reported point-of-care chips to quantitatively measure CD64 expression for sepsis stratification. Both of these CD64 capture designs showed promise for sepsis diagnosis, but were limited to a single measurement parameter. Any single biomarker may be influenced by infections that do not lead to sepsis. For example, CD64-based measurements assay neutrophils and monocytes, but do not measure lymphocyte activation in sepsis. CD25 and CD69 are primarily upregulated in lymphocytes during sepsis. The combination of multiple parameters could overcome drawbacks of a single parameter. In this work, a multi-parameter cell affinity separation chip was developed for sepsis diagnosis (Figure 1). CD64, CD69, and CD25 were chosen as recognition biomarkers to capture leukocytes with changes in antigen expression on cell surface during sepsis. Cell count thresholds for CD25, CD64, and CD69 were established using healthy volunteers as a control group. Performance of the multi-parameter separation chips was compared to flow cytometry analysis as well as clinical vital sign monitoring. Of these three biomarkers, the expression of 5



CD64 and CD69 showed significant upregulation in septic patients indicating their potential to differentiate sepsis from healthy conditions. We also observed that on-chip capture of CD64+ or CD69+ leukocytes from blood was linear to antigen expression level. Furthermore, Receiver Operating Characteristic (ROC) analysis showed that combined panel of CD64+ and CD69+ leukocyte capture was the most accurate indicator among each single parameter (ROC=0.98).

A

C

B

D

Figure 1. Multi-parameter cell capture microfluidics for sepsis diagnosis. A: Schematic of multi-region U-shaped microfluidic chips. Eight gas valves was applied to separate the main channel into 4 regions for different antibody coating. B: Schematic of surface modification on main channel of the U shape microchip. CD64+, CD69+, and CD25+ leukocytes can be captured simultaneously on each corresponding affinity region on a single device. Each affinity region had a dimension of 1 cm x 1 mm x 40 µm (L x W x H). C: Schematic of cell capture in one affinity region, using CD64+ cells as an example. When lysed blood samples 6


Page 6 of 27

Page 7 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


pass through the affinity region, CD64+ cells were captured by anti-CD64 antibodies coated on the chip surface. The process of affinity capture in the CD69 region and CD25 region were identical, using either anti-CD69 or anti-CD25 antibodies, respectively. D: Photograph of a multi-parameter capture device, with food dyes injected into the capture regions for visualization.

Experimental Microfluidic Device Fabrication. Conventional multi-layer soft lithography methods were used in this work to fabricate microfluidic chips.46,47 Microfluidic chip designs were first drawn using Adobe Illustrator and then printed at high resolution on masks (20,000 dpi laser printer transparency by CAD/Art Services). Molds for creating channels on fluidic layers and control layers were created via photolithography. Negative photoresist SU-8 2015 (Micro Chem) was deposited on a 100 mm silicon wafer (University Wafer) and spin coated at 500 rpm for 6 s, followed by 1000 rpm for 30 s, which formed a layer of photoresist with 40 µm thick. Wafers were then processed using 6 min of pre-bake at 95 °C, 15 s of UV exposure, 6 min of post-bake at 95 °C, 6 min of developing with SU-8 developer (Micro Chem), and 30 min of baking at 200 °C to ensure mechanical strength. Wafers were treated with 1H,1H,2H,2H-perfluorooctyltrichlorosilane (Alfa Aesar) in a desiccator overnight to form a hydrophobic surface which promoted removal of poly-dimethylsiloxane (PDMS) from the wafer. Chip layers containing control valves were fabricated using a mixture of PDMS prepolymer and curing agent (Ellesworth Adhesives) at a weight ratio of 5:1. This mixture 7



was poured onto the wafer and baked at 95 °C for 1 hr. The chip layer for cell separation channels was fabricated in the same fashion using a 25:1 prepolymer-curing agent ratio. This mixture was spin coated on wafer at 2000 rpm for 30 s and baked at 70 °C for 30 min. The cured control layer was peeled off of the wafer and sealed onto semi-solidified fluidic layer at 120 °C for another 2 hr. The finished PDMS channels were removed from the wafers and inlet and outlet holes were punched for all channels and valves. The completed PDMS channels were bonded to glass using oxygen plasma (Harrick). 30-gauge poly-(tetrafluoroethylene) (PTFE) tubing (Small Parts) was connected to inlets and outlets to finish device assembly. As showed in Figure 1, the multi-parameter chip has two separate layers. The top layer contains 8 gas valves and the bottom layer contains 8 side channels and one main channel. Side channels were first blocked by sealed capillary tubes to coat the affinity regions with biotinylated-BSA and neutravidin. Next, air was pumped into all gas valves to close the main separation channel into four different regions for antibody coating. The side channels in the bottom fluidic layer were the source and waste for each of the four antibody regions. This approach allowed us to create well-defined antibody zones in the main separation channel.

Affinity Surface Modification. The affinity capture surface on the microfluidic chip was prepared using sandwich deposition approaches.48-52 Microfluidic chips were first rinsed with deionized water and air dried. A solution of 1 mg/mL biotinylated bovine serum albumin (Biotin-BSA, Sigma-Aldrich) (1 mg/mL, in 10 mM Tris-HCl, pH 8.0, 50 mM NaCl) was introduced into the channel and incubated for 45 min at room temperature. The channel was then rinsed with T50 buffer (10 mM Tris-HCl buffer) and air dried. A 0.2 mg/mL neutravidin 8


Page 8 of 27

Page 9 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(Pierce) solution (0.2 mg/mL in 10 mM Tris-HCl buffer) was loaded into the channel and incubated for 20 min, followed by rinsing with T50 buffer and deionized water and stored at 4 °C until needed. Prior to testing each septic or healthy sample, 3.5 µL of 6.25 µg/mL biotinylated antibody (BD Biosciences) were injected into each affinity region and incubated for 20 min to finish affinity surface modification. Antibody coatings were confined to their respective regions via control pneumatic valves during the coating process. The three affinity regions were coated in the following order: biotinylated mouse anti-human CD64 antibody (Clone: 10.1; Catalog No.555526 from BD Bioscience), biotinylated mouse anti-human CD69 antibody (Clone: FN50; Catalog No.13-0699-82 from eBioscience), and biotinylated mouse anti-human CD25 antibody (Clone: BC96 Catalog No.13-0259-82 from eBioscience). Excess antibodies were flushed out with air bubbles and the chip was filled with 3% BSA in phosphate-buffered saline (PBS) solution before on chip analysis to minimize nonspecific binding. The end result of this process was three spatially-defined cell capture regions for the three parameters under investigation.

Septic and Healthy Blood Sample Preparation. The study of clinical patient blood samples was reviewed and approved by the Texas Tech University Health Sciences Center Institutional Review Board. Universal precautions were taken for the safe handling of blood samples. Clinical blood samples used in this study include blood from healthy volunteers and septic patients. Both types of blood were collected from the University Medical Center (UMC) in Lubbock, TX. Informed consent was obtained from all volunteers and septic patients. Blood samples from septic patients were collected from patients who were 9



undergoing treatment in the surgical intensive care unit or identified through the UMC Code Sepsis Program. Patients were diagnosed with sepsis according to qSOFA (quick Sepsis Related Organ Failure Assessment) scores.53 The qSOFA score uses three criteria, assigning one point for (i) altered mental status (Glasgow coma scale < 15), (ii) systolic blood pressure ≤ 100 mmHg, and (iii) respiratory rate ≥ 22/min. Patients were identified and treated as septic with a qSOFA score ≥ 2. Patient blood samples were collected within 24 hours of diagnosis (limited by logistical demands of the hospital) as well as 48 hours after the 1st collection. Healthy volunteers were solicited from the University Medical Center. Healthy volunteers were screened to have no acute or chronic medical conditions and took no medication except birth control. Blood samples were collected in 4 mL BD Vacutainer™ Plastic Blood Collection Tubes with K2EDTA (Fisher Scientific) and stored at 4 °C until analysis. For analysis, the blood was shaken and equilibrated to room temperature. 100 µL of whole blood was mixed with 900 µL of deionized water for 30 s to lyse erythrocytes. Immediately after lysing, 110 µL of concentrated saline buffer (10X) was added to the diluted blood to restore osmolarity. The mixture was centrifuged at 4500 rpm for 5 min. Lysed erythrocytes in the supernatant were then discarded and leukocytes in the precipitate were resuspended with 100 µL PBS buffer or 3% BSA (in PBS) buffer to their original concentration for analysis.54

Microfluidic Cell Capture and Detection. For better visualization under the microscope, lysed blood samples were stained with Hoechst 33342 (1µL/mL, Thermo Fisher Scientific). Lysed blood samples were introduced into chips using a syringe pump (KD Scientific). Once the blood sample had completely filled the separation channel, the syringe pump was stopped 10


Page 10 of 27

Page 11 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


and cells were allowed to settled on channel surface for 30 min.49 3% BSA in PBS solution was then injected into the channel via syringe pump at a flow rate of 0.03 mL/h for 20 min to remove weakly bound or unbound cells and was kept flowing through the channel during image acquisition to differentiate captured cells from cells floating in the channel. Captured cells were observed using an inverted epifluorescence microscope (IX71, Olympus) with filters for Hoechst 33342 and a 0.3 NA 10X objective lens. Images were obtained with a cooled charge coupled device (CCD) camera (Hamamatsu) coupled to the microscope and analyzed using ImageJ software (Version 1.43u, National Institutes of Health). Captured blood cells in each affinity region were identified and enumerated. We used a threshold based on the mean cell capture count (µ) in each antibody region and the standard deviation of each capture count (σ). Cell counts exceeding the threshold (µ + 3σ) were classified as septic for comparison with clinical diagnosis and bacterial culture. Capture efficiency was calculated as (Equation 1): Capture Efficiency =

Ncap NSum

×100%

(Eq. 1)

where Ncap is the number of captured cells on chip and NSum is the calculated number of total blood cells loaded into channel. This value is based on the cell concentration per square micrometer in the chip after loading. Error bars in all figures represent the standard deviation of each measurement.

Flow Cytometry Analysis. Flow cytometry (FACSCalibur, Becton Dickinson) analysis was conducted together with on-chip cell capture to measure antigen expression for CD25, CD64, 11



and CD69. Four aliquots of 100 µL lysed blood were stained for CD25, CD64, and CD69, and unstained (control) respectively. Biotin-antiCD25, biotin-antiCD64, and biotin-antiCD69 were added to corresponding blood samples and incubated at 37 °C for 20 min. Samples were washed three times with PBS buffer by centrifugation at 4500 rpm for 5 min. Each sample was resuspended in 100 µL PBS buffer and 1 µL of allophycocyanin (APC)-streptavidin (1 mg/mL, Invitrogen) was added to each blood sample for fluorophore labeling and incubated at 37 °C for

20 min. Excess dye was washed off three times with PBS buffer by centrifugation and resuspension. Each sample was then resuspended in 500 µL PBS buffer for flow cytometry

analysis. The relative cell surface antigen expression was determined using the geometric mean of APC fluorescence intensity.

Data analysis. Fluorescent and white light cell counting was performed using Image J (National Institutes of Health). Origin 8 was employed for data analysis. Auto CAD and Adobe Illustrator were employed for the chip design.

Results and Discussion On-Chip Analysis of Healthy and Septic Samples Fifteen patient samples were studied for multi-parameter cell capture. All 15 patients had a qSOFA score of 2 or more. Clinical data was recorded in Table S1. All healthy volunteers had qSOFA score less than 2. For septic patient samples, the total CD64+ captured cell count had a range of 85 to 687, combining both 1st and 2nd blood draws (Figure 2.C). The total CD64+ cell count range for healthy volunteers was 6 to 82. The difference of on-chip 12


Page 12 of 27

Page 13 of 27

cell capture between healthy volunteers and septic patients (including 1st blood draw and 2nd blood draw) are statistically significant (p=0.0033 for 1st blood draw, p=0.0121 for 2nd blood draw, 95% confidence interval). Similar to CD64+ cell capture, CD69+ cell capture showed significant increase with septic patients compared to healthy volunteers (Figure 2.D, p=0.0221 for 1st blood draw, p=0.0800 for 2nd blood draw, 95% confidence interval). CD69+ cell capture for septic patient samples ranged from 32 to 551 and for healthy volunteers ranged from 9 to 71(Figure 2.D). However, the on-chip cell capture of CD25+ cells did not show significant difference between healthy controls and septic patients (Figure 2.E, p=0.1046, 95% confidence interval). Therefore, in this work, we consider CD64 and CD69 as effective diagnostic markers for sepsis.

A

B 700

600

600

500

Number of Captured Cells


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


500 400 300 200 100

300

200

100

0

0 Healthy Control

C

400

Patient 1st Draw

Patient 2nd Draw

Healthy Control

D

13


Patient 1st Draw

Patient 2nd Draw


600


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

400

200

0 Healthy Control

Patient 1st Draw

Patient 2nd Draw

E Figure 2. Cell capture comparison between septic patients (n=15) and healthy volunteers (n=10) on the multi-parameter sepsis chip. A: White light image with captured cells circled in red. Captured cells were randomly distributed in the affinity region. B: Fluorescence image identifying captured cells with Hoechst 33342 staining. Scale bar = 50 µm. Cell counts from the chip (C: CD64 region; D: CD69 region; E: CD25 region). The first blood draw was obtained within 24 hours of sepsis diagnosis and the second draw within the next 48 hours. The CD64+ cell capture difference between healthy volunteers and septic patients was significant (p=0.0033 for 1st blood draw, p=0.0121 for 2nd blood draw, 95% confidence interval). In CD69 region, the capture difference between healthy volunteers and septic patients was also significant (p=0.0221 for 1st blood draw, p=0.0800 for 2nd blood draw, 95% confidence interval). However, in the CD25 region, there is no significant difference between healthy volunteers and septic patients (p=0.1046, 95% confidence interval). Error bars represent the standard deviation.

Using qSOFA as the standard reference for sepsis diagnosis, Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the accuracy of on-chip detection of CD64+ and CD69+ cells for sepsis diagnosis. In this study, the area under ROC curve (AUC) 14


Page 14 of 27

Page 15 of 27

was 0.95 for CD64 region (Figure 3.A), indicating the excellent accuracy of on-chip detection when CD64 serving as one single diagnostic biomarker. Comparing with our previous work which using CD64 as a single diagnostic biomarker,40 the ROC analysis of cell capture on U shape chip showed that the change of device would not compromise the predictive ability of CD64 for sepsis diagnosis. For CD69+ cell capture, the AUC was 0.75 (Figure 3.B), which was not as satisfactory as CD64 detection but still showed fair diagnostic ability. Logistic regression was conducted to evaluate the diagnostic ability of the combination of CD64 and CD69 expression based cell capture as biomarkers for sepsis.55 The AUC of the combined panel with CD64 and CD69 was 0.98 (Figure 3.C). This result showed that the combination of CD64 and CD69 outperformed both biomarkers when they were tested alone. 1.0

1.0

0.8

0.8

AUC=0.95

0.6

Sensitivity

Sensitivity

AUC=0.75

0.4

0.2

0.6

0.4

0.2

CD64 as Sepsis Diagnostic Biomarker on 4-Region Chip

0.0 0.0

0.2

0.4

0.6

0.8

A

CD69 as Sepsis Diagnostic Biomarker on 4-Region Chip

0.0

1.0

1-Specificity

0.0

0.2

B

AUC=0.98 0.8

0.6

0.4

0.2

CD64 and CD69 as Sepsis Diagnositic Biomarker on 4-Region Chip

0.0 0.0

C

0.2

0.4

0.6

0.8

0.4

0.6

1-Specificity

1.0

Sensitivity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1.0

1-Specificity

15


0.8

1.0


Figure 3. Receiver Operating Characteristic analysis of the 1st blood draw from septic patients in the multi-parameter sepsis chip (A: CD64 region; B: CD69 region; C: combination of CD64 and CD69 biomarkers). The area under the curve (AUC) in the CD64 region was 0.95, indicating excellent predicting value. The AUC in the CD69 region was 0.75, which has a fair predictive performance. The AUC for combined two parameters was 0.98, indicating not only higher accuracy of on-chip detection, but also the superiority of two biomarkers over either single biomarker.

On-chip detection was compared to flow cytometry results of CD64 and CD69 expression. CD64 and CD69 expression in septic patients was normalized to unstained controls and CD64+ and CD69+ cell counts were normalized to the capture efficiency to reduce variances caused by initial cell concentration and other variables. Normalized data was only employed for comparison between flow cytometry results and cell capture results. CD64 expression showed good linear relationship with cell capture in the CD64 region (y=0.30x+1.24, R2=0.88) while CD69 expression showed adequate linearity with cell capture in the CD69 region (y=0.41x+0.59, R2=0.77) (Figure 4), which was in agreement with our previous work on cell capture and antigen expression.40 This linear relationship accounts for the increase of cell capture in sepsis. Healthy samples (Figure 4) can be distinguished from septic patients based on cell capture counts. The overlap area of healthy volunteers and septic patients in the CD64 region was smaller than that of the CD69 region, indicating that CD64+ cell capture is more reliable parameter.

16


Page 16 of 27

16

16

14

14

12

12

10 8

Septic Patient

6 4

Healthy Control

2 0 0

A

Capture Efficiency (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Capture Efficiency (%)

Page 17 of 27

10

20

30

Septic Patient

10 8 6 4

Healthy Control

2

40

0 0

50

CD64 Expression Ratio (Sample/Control)

B

10

20

30

40

50

CD69 Expression Ratio (Sample/Control)

Figure 4. Linear relationship between CD64 expression and cell capture in CD64 region (A) and CD69 expression and cell capture in CD69 region (B) on U shape chip. For the CD64 region, the linear regression was y=0.30x+1.24, R2=0.88; for the CD69 region, the linear regression was y=0.41x+0.59, R2=0.77. CD64+ cell capture had less overlap between healthy and septic samples.

Comparison with Clinical Blood Culture On-chip detection was also compared with clinical specimen culture for suspected pathogens. Of the 15 septic patients in this study, 12 samples were compared because 3 of the patient samples were missing vital signs. Those 3 patients fell outside of the sample collection window of our IRB protocol. Both infection site cultures and blood cultures were conducted on all 12 patients. However, only 3 out of these 12 patients showed positive blood culture within 72 hr study window, and 7 out of 12 patients showed positive infection sites culture (Table 1). At the same time, all 15 septic patient on-chip analyses showed significant increase in CD64+ and CD69+ cell counts compared to healthy volunteers. Besides, the analysis time of on-chip detection was about 2 hr—and can be improved—which is much shorter than specimen culture (> 24 hr). Since it is the physiological response to infection that 17



Page 18 of 27

causes sepsis, detection of the pathogen is secondary to stabilizing the patient. Our method measures immunological response and can therefore be used to trigger sepsis therapies such as fluid resuscitation. The quantitative nature of our panel eliminates the need for clinical scoring as a sepsis threshold. The decreased diagnosing time can not only allow clinicians to start correct treatment, which is important to reduce mortality in sepsis, but also to prevent the use of broad spectrum antibiotic treatment when it is not necessary.

Table 1. Comparison between on-chip detection and specimen cultures Blood Culture (+)

Infection Site Culture (+)

Chip Detection (+)

Number

3

7

12

Percentage

25%

58%

100%

*Total sample number: 12

Flow Cytometry Analysis of Healthy and Septic Samples Flow cytometry analysis was conducted to test CD25, CD64, and CD69 expression in blood samples of healthy volunteers and septic patients as a control to validate on-chip cell capture analysis. The same 15 septic patient samples were measured for CD64 expression as a function of cell type (neutrophils, monocytes, and lymphocytes; Figure 5). The expression of CD64 on lymphocytes remained low while it increased significantly in both neutrophils and monocytes. The upregulation of CD64 on neutrophils is a result of neutrophil activation during pro-inflammatory response from infections. CD64 expression on healthy volunteers

18


Page 19 of 27

and septic patients showed significant difference (p=0.0080) (Figure 6.A). CD64 expression quadruples in septic patients compared to healthy volunteers on average. 9

Lymphocyte Neutrophil Monocyte

8

Number of samples

7

Number of samples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


6 5 4 3 2

9 8 7 6 5 4 3 2 1 0 0

Lymphocyte

100

200

300

1 0 0

1000

2000

3000

4000

CD64 Expression

Figure 5. Flow cytometry analysis of CD64 expression on leukocytes from septic patients. The inset histogram represents lymphocyte CD64 expression. CD64 expression on lymphocytes is low while on neutrophils and monocytes it is higher due to immune system response to infection.

In agreement with previous literature, the average CD69 expression in septic patients increased two-fold compared to healthy volunteers (Figure 6.B).34,35 The difference of CD69 expression between septic patients and healthy volunteers was considered to be significant (p=0.0139), indicating that CD69 expression could be a biomarker in early diagnosis of sepsis. However, the change in CD25+ cell population between two groups did not show significant difference (p= 0.8886, Figure 6C), in agreement with our chip results.

19



4

4

Healthy Control Patient Sample

Healthy Control Patient Sample

3

Number of Samples

Number of Sample

3

2

1

0

2

1

0 0

100

200

300

400

500

600

CD64 Expression

A

20

40

60

80

100

120

CD69 Expression

B Healthy Control Patient Sample

4

3

Number of Samples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 27

2

1

0 0

10

20

30

40

50

60

Population (%) of CD25+ Leukocytes

C

Figure 6. A: Flow cytometry analysis of neutrophil CD64 expression from healthy volunteers and septic patients. The ranges of CD64 expression (arbitrary units) for healthy samples and septic samples were 15 to 80, and 69 to 562, respectively. The difference of CD64 expression between the two groups was extremely statistically significant. B: Flow cytometry analysis of CD69 expression on healthy and septic samples. The increase of CD69 on septic patients was significant compared to healthy volunteers. C: Flow cytometry analysis of CD25+ leukocytes. There was no strong statistical difference between healthy control and patient samples.

A second draw of blood was taken for each patient 48 hr after the first draw to monitor sepsis progress. One patient (Patient 8) expired within 48 hr due to fast deterioration of 20


Page 21 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


sepsis, resulting in only a single blood draw in that instance. The change of CD64 expression between the 1st and 2nd blood draw was shown on Figure 7.A. Among 14 septic patients, half of them showed improved vital signs (Patient 1, 2, 5, 7, 11, 13, and 15), 5 of them showed worsened condition (Patient 3, 4, 9, 10, and 12), and the remaining 2 patients (Patient 6 and 14) showed stable condition (Table S1). The changes in CD64 expression agreed with the development of sepsis in 12 cases. The change in CD69 expression between 1st blood draw and 2nd blood draw was less accurate, with 9 cases in agreement with vital sign changes (Figure 7.B). Therefore, CD64 is not only a promising indicator for sepsis diagnosis, but also an indicative biomarker for recovery outcomes,38 while CD69 is less accurate than CD64 in outcome predictive ability. Moreover, since on-chip cell capture has linear relationship with antigen expression, and the expression of CD64 and CD69 can serve as prognosis biomarker as well, the progression of sepsis can also be monitored using these multi-parameter chips. The total captured cell number on microfluidic chips provides a label-free and quantitative measurement for sepsis diagnosis and prognosis.

A

B

Figure 7. A: Changes in CD64 expression between 1st blood draw and 2nd blood draw of septic patients. 12 out of 14 cases have shown consistent results between CD64 expression and clinical vital signs. B: Changes in CD69 expression between 1st blood draw and 2nd blood 21



draw of septic patients. 9 out of 14 cases have shown that changes in CD69 expression were in agreement with sepsis development. Expression of CD64 showed higher prognosis value than CD69. Asterisk labeled patients are those who had improved vital signs.

Conclusion We have developed a microfluidic, multi-parameter assay based on CD64+ and CD69+ expression for sepsis diagnosis. On-chip cell capture for both CD64+ and CD69+ cells showed good linear relationships with corresponding antigen expression. Microfluidic tests of blood samples from septic patients showed significant difference with samples from healthy volunteers. For on-chip detection of CD64+ cells from septic patient blood samples, the AUC indicated the excellent accuracy of on-chip detection. The comparison between CD64 expression change and difference in patient vital signs showed that CD64 was also a good indicator for monitoring sepsis progression. Although CD69 expression change in sepsis is not as sensitive as CD64, the detection of CD69 expression still has diagnostic value and can serve as a complementary biomarker for sepsis diagnosis. ROC analysis of the combination of CD64 and CD69 detection showed that simultaneous testing of multiple biomarkers provided a more accurate and reliable conclusion for sepsis diagnosis. Our microfluidic approach measures total on-chip cell counts as a label-free and quantitative detection for sepsis diagnosis and prognosis. While ventilation and vasopressors impact clinical vital signs immediately,56 the immune system response is not impacted and can be used for detection and patient monitoring. The total detection process is much faster than clinical specimen culture and is simple to use, thus it is especially suitable for clinical application. In the future, blood 22


Page 22 of 27

Page 23 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


samples drawn from patients can be introduced to microfluidic chip directly, which would further decrease the analysis time. The integration of all functions, including sample preparation and cell capture, to one microfluidic device will result in a true point-of-care analysis of sepsis.

Supporting Information. CD25 Cell capture metrics and clinical vital signs for all patients are available as supporting information.

Acknowledgements This work was supported by a grant from The CH Foundation.

23



References (1) Singer, M.; Deutschman, C. S.; Seymour, C. W.; Shankar-Hari, M.; Annane, D.; Bauer, M.; Bellomo, R.; Bernard, G. R.; Chiche, J. D.; Coopersmith, C. M.; Hotchkiss, R. S.; Levy, M. M.; Marshall, J. C.; Martin, G. S.; Opal, S. M.; Rubenfeld, G. D.; van der Poll, T.; Vincent, J. L.; Angus, D. C. Jama 2016, 315, 801-810. (2) Jawad, I.; Luksic, I.; Rafnsson, S. B. Journal of global health 2012, 2, 010404. (3) Dombrovskiy, V. Y.; Martin, A. A.; Sunderram, J.; Paz, H. L. Critical care medicine 2007, 35, 1244-1250. (4) Kochanek, K. D.; Murphy, S. L.; Xu, J.; Tejada-Vera, B. National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System 2016, 65, 1-122. (5) Lagu, T.; Rothberg, M. B.; Shieh, M. S.; Pekow, P. S.; Steingrub, J. S.; Lindenauer, P. K. Critical care medicine 2012, 40, 754-761. (6) Hotchkiss, R. S.; Karl, I. E. N Engl J Med 2003, 348, 138-150. (7) Mayr, F. B.; Talisa, V. B.; Balakumar, V.; Chang, C. H.; Fine, M.; Yende, S. Jama 2017, 317, 530531. (8) Gullo, A.; Bianco, N.; Berlot, G. Critical care clinics 2006, 22, 489-501, ix. (9) Lever, A.; Mackenzie, I. BMJ (Clinical research ed.) 2007, 335, 879-883. (10) Kirn, T. J.; Weinstein, M. P. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases 2013, 19, 513-520. (11) Kumar, A.; Roberts, D.; Wood, K. E.; Light, B.; Parrillo, J. E.; Sharma, S.; Suppes, R.; Feinstein, D.; Zanotti, S.; Taiberg, L.; Gurka, D.; Kumar, A.; Cheang, M. Critical care medicine 2006, 34, 15891596. (12) Zambon, M.; Ceola, M.; Almeida-de-Castro, R.; Gullo, A.; Vincent, J. L. Journal of critical care 2008, 23, 455-460. (13) Darby, J. M.; Linden, P.; Pasculle, W.; Saul, M. Critical care medicine 1997, 25, 989-994. (14) Sankar, V.; Webster, N. R. Journal of anesthesia 2013, 27, 269-283. (15) Vincent, J. L.; Donadello, K.; Schmit, X. Critical care clinics 2011, 27, 241-251. (16) Limongi, D.; D'Agostini, C.; Ciotti, M. Asian Pacific Journal of Tropical Biomedicine 2016, 6, 516-519. (17) Pierrakos, C.; Vincent, J. L. Critical care (London, England) 2010, 14, R15. (18) Couto, R. C.; Barbosa, J. A.; Pedrosa, T. M.; Biscione, F. M. Brazilian Journal of Infectious Diseases 2007, 11, 240-245. (19) Schmit, X.; Vincent, J. L. Infection 2008, 36, 213-219. (20) Ho, K. M.; Dobb, G. J.; Lee, K. Y.; Towler, S. C.; Webb, S. A. Journal of critical care 2006, 21, 259-265. (21) da Silva Silvestre, J. P.; da Cruz Coelho, L. M.; Póvoa, P. M. S. R. Journal of critical care 2010, 25, 657. e657-657. e612.

24


Page 24 of 27

Page 25 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(22) Giamarellos-Bourboulis, E. J.; Giannopoulou, P.; Grecka, P.; Voros, D.; Mandragos, K.; Giamarellou, H. Journal of critical care 2004, 19, 152-157. (23) Tang, B. M.; Eslick, G. D.; Craig, J. C.; McLean, A. S. The Lancet. Infectious diseases 2007, 7, 210-217. (24) Uzzan, B.; Cohen, R.; Nicolas, P.; Cucherat, M.; Perret, G.-Y. Critical care medicine 2006, 34, 1996-2003. (25) Cid, J.; Aguinaco, R.; Sanchez, R.; Garcia-Pardo, G.; Llorente, A. The Journal of infection 2010, 60, 313-319. (26) de Jong, E.; de Lange, D. W.; Beishuizen, A.; van de Ven, P. M.; Girbes, A. R.; Huisman, A. International journal of laboratory hematology 2016, 38, 576-584. (27) Dimoula, A.; Pradier, O.; Kassengera, Z.; Dalcomune, D.; Turkan, H.; Vincent, J. L. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2014, 58, 820-829. (28) Lam, H. S.; Cheung, H. M.; Poon, T. C.; Wong, R. P.; Leung, K. T.; Li, K.; Ng, P. C. Clinical chemistry 2013, 59, 1753-1760. (29) Li, S.; Huang, X.; Chen, Z.; Zhong, H.; Peng, Q.; Deng, Y.; Qin, X.; Zhao, J. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 2013, 17, e12-23. (30) Rogina, P.; Stubljar, D.; Lejko-Zupanc, T.; Osredkar, J.; Skvarc, M. Clinical chemistry and laboratory medicine 2015, 53, e89-91. (31) Leng, F.-Y.; Liu, J.-L.; Liu, Z.-J.; Yin, J.-Y.; Qu, H.-P. Journal of Microbiology, Immunology and Infection 2013, 46, 338-344. (32) Siqueira-Batista, R.; Gomes, A. P.; Azevedo, S. F. M.; Vitorino, R. R.; Mendonça, E. G. d.; Sousa, F. O. d.; Oliveira, A. d. P.; Cerqueira, F. R.; Paula, S. O. d.; Oliveira, M. G. d. A. Revista Brasileira de terapia intensiva 2012, 24, 294-301. (33) Chen, K.; Zhou, Q. X.; Shan, H. W.; Li, W. F.; Lin, Z. F. World journal of emergency medicine 2015, 6, 40-43. (34) Boomer, J. S.; Shuherk-Shaffer, J.; Hotchkiss, R. S.; Green, J. M. Critical care 2012, 16, R112125. (35) Labib, A. Z.; Mahmoud, A. B.; Eissa, N.; El Gendy, F. M.; Soliman, M. A.; Aly, A. A. Int. J. Microbiol. Res 2013, 4, 77-85. (36) Schwulst, S. J.; Muenzer, J. T.; Chang, K. C.; Brahmbhatt, T. S.; Coopersmith, C. M.; Hotchkiss, R. S. Journal of the American College of Surgeons 2008, 206, 335-342. (37) El-Ali, J.; Sorger, P. K.; Jensen, K. F. Nature 2006, 442, 403-411. (38) Hassan, U.; Ghonge, T.; Reddy Jr, B.; Patel, M.; Rappleye, M.; Taneja, I.; Tanna, A.; Healey, R.; Manusry, N.; Price, Z.; Jensen, T.; Berger, J.; Hasnain, A.; Flaugher, E.; Liu, S.; Davis, B.; Kumar, J.; White, K.; Bashir, R. 2017, 8, 15949. (39) Prieto, J. L.; Su, H. W.; Hou, H. W.; Vera, M. P.; Levy, B. D.; Baron, R. M.; Han, J.; Voldman, J. Lab Chip 2016, 16, 4333-4340. (40) Zhang, Y.; Li, W.; Zhou, Y.; Johnson, A.; Venable, A.; Hassan, A.; Griswold, J.; Pappas, D. Analyst 2018, 143, 241-249. (41) Kemmler, M.; Sauer, U.; Schleicher, E.; Preininger, C.; Brandenburg, A. Sensors and Actuators B: Chemical 2014, 192, 205-215. (42) Buchegger, P.; Preininger, C. Biomedizinische Technik. Biomedical engineering 2013. (43) Baldini, F.; Bolzoni, L.; Giannetti, A.; Kess, M.; Kramer, P. M.; Kremmer, E.; Porro, G.; Senesi, F.; Trono, C. Analytical and bioanalytical chemistry 2009, 393, 1183-1190. (44) Lu, X.; Lv, C.; Qu, Y.; Luo, Y.; Lin, B.; Zhan, L.; Song, Y.; Fan, Z. Immunology letters 2016, 173, 55-60. (45) Guan, X.; Zhang, H. J.; Bi, Y. N.; Zhang, L.; Hao, D. L. Biomedical microdevices 2010, 12, 683-

25



691. (46) Duffy, D. C.; McDonald, J. C.; Schueller, O. J.; Whitesides, G. M. Anal Chem 1998, 70, 49744984. (47) Unger, M. A.; Chou, H. P.; Thorsen, T.; Scherer, A.; Quake, S. R. Science (New York, N.Y.) 2000, 288, 113-116. (48) Li, P.; Gao, Y.; Pappas, D. Anal Chem 2011, 83, 7863-7869. (49) Li, P.; Gao, Y.; Pappas, D. Anal Chem 2012, 84, 8140-8148. (50) Li, W.; Gao, Y.; Pappas, D. Biomedical microdevices 2015, 17, 113. (51) Wang, K.; Cometti, B.; Pappas, D. Analytica chimica acta 2007, 601, 1-9. (52) Zhang, Y.; Pappas, D. Analyst 2016, 141, 1440-1447. (53) Bhattacharjee, P.; Edelson, D. P.; Churpek, M. M. Chest 2016. (54) Wang, K.; Marshall, M. K.; Garza, G.; Pappas, D. Analytical Chemistry 2008, 80, 2118-2124. (55) Steyerberg, E. W.; Eijkemans, M. J. network 2000, 10, 11. (56) Avni, T.; Lador, A.; Lev, S.; Leibovici, L.; Paul, M.; Grossman, A. PLoS ONE 2015, 10, e0129305.

26


Page 26 of 27

Page 27 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


For TOC Only

27


Multiparameter Affinity Microchip for Early Sepsis Diagnosis Based on

Recommend Documents