Improved cytometric analysis of untouched lung leukocytes by enzymatic liquefaction of sputum samples.

BACKGROUND: Phenotyping sputum-resident leukocytes and evaluating their functional status are essential analyses for exploring the cellular basis of pathological processes in the lungs, and flow cytometry is widely recognized as the gold-standard technique to address them. However, sputum-resident leukocytes are found in respiratory samples which need to be liquefied prior to cytometric analysis. Traditional liquefying procedures involve the use of a reducing agent such as dithiothreitol (DTT) in temperature-controlled conditions, which does not homogenize respiratory samples efficiently and impairs cell viability and functionality. METHODS: Here we propose an enzymatic method that rapidly liquefies samples by means of generating O2 bubbles with endogenous catalase. Sputum specimens from patients with suspected pulmonary infection were treated with DTT, the enzymatic method or PBS. We used turbidimetry to compare the liquefaction degree and cell counts were determined using a hemocytometer. Finally, we conducted a comparative flow cytometry study for evaluating frequencies of sputum-resident neutrophils, eosinophils and lymphocytes and their activation status after liquefaction. RESULTS: Enzymatically treated samples were better liquefied than those treated with DTT or PBS, which resulted in a more accurate cytometric analysis. Frequencies of all cell subsets analyzed within liquefied samples were comparable between liquefaction methods. However, the gentle cell handling rendered by the enzymatic method improves cell viability and retains in vivo functional characteristics of sputum-resident leukocytes (with regard to HLA-DR, CD63 and CD11b expression). CONCLUSION: In conclusion, the proposed enzymatic liquefaction method improves the cytometric analysis of respiratory samples and leaves the cells widely untouched for properly addressing functional analysis of lung leukocytes.

Plasma-induced nanoparticle aggregation for stratifying COVID-19 patients according to disease severity.

Stratifying patients according to disease severity has been a major hurdle during the COVID-19 pandemic. This usually requires evaluating the levels of several biomarkers, which may be cumbersome when rapid decisions are required. In this manuscript we show that a single nanoparticle aggregation test can be used to distinguish patients that require intensive care from those that have already been discharged from the intensive care unit (ICU). It consists of diluting a platelet-free plasma sample and then adding gold nanoparticles. The nanoparticles aggregate to a larger extent when the samples are obtained from a patient in the ICU. This changes the color of the colloidal suspension, which can be evaluated by measuring the pixel intensity of a photograph. Although the exact factor or combination of factors behind the different aggregation behavior is unknown, control experiments demonstrate that the presence of proteins in the samples is crucial for the test to work. Principal component analysis demonstrates that the test result is highly correlated to biomarkers of prognosis and inflammation that are commonly used to evaluate the severity of COVID-19 patients. The results shown here pave the way to develop nanoparticle aggregation assays that classify COVID-19 patients according to disease severity, which could be useful to de-escalate care safely and make a better use of hospital resources.

Rapid Identification and Classification of Pathogens That Produce Carbapenemases and Cephalosporinases with a Colorimetric Paper-Based Multisensor.

Infections caused by bacteria that produce ß-lactamases (BLs) are a major problem in hospital settings. The phenotypic detection of these bacterial strains requires culturing samples prior to analysis. This procedure may take up to 72 h, and therefore it cannot be used to guide the administration of the first antibiotic regimen. Here, we propose a multisensor for identifying pathogens bearing different types of ß-lactamases above the infectious dose threshold within 90 min that does not require culturing samples. Instead, bacterial cells are preconcentrated in the cellulose scaffold of a paper-based multisensor. Then, 12 assays are performed in parallel to identify whether the pathogens produce carbapenemases and/or cephalosporinases, including metallo-ß-lactamases, extended-spectrum ß-lactamases (ESBLs), and AmpC enzymes. The multisensor generates an array of colored spots that can be quantified with image processing software and whose interpretation leads to the detection of the different enzymes depending on their specificity toward the hydrolysis of certain antibiotics, and/or their pattern of inhibition or cofactor activation. The test was validated for the diagnosis of urinary tract infections. The inexpensive paper platform along with the uncomplicated colorimetric readout makes the proposed prototypes promising for developing fully automated platforms for streamlined clinical diagnosis.

Colorimetric Detection of Sepsis-Derived Hyperdegranulation with Plasmonic Nanosensors.

Hyperdegranulation of neutrophilic granulocytes is a common finding in sepsis that directly contributes to the heightened immune response leading to organ dysfunction. Currently, cell degranulation is detected by flow cytometry, which requires large infrastructure that is not always available at the point of care. Here, we propose a plasmonic assay for detecting the degranulation status of septic cells colorimetrically. It is based on triggering the aggregation of gold nanoparticles with cationic granule proteins. Cells from septic patients contain fewer granules and therefore release less cationic proteins than healthy cells. This results in red-colored assays than can be easily detected by eye. The assay can selectively detect cationic granule proteins even in the presence of an excess of unrelated proteins, which is key to detect degranulation with high specificity. Coupling this signal generation mechanism with a magnetic purification step enabled the identification of septic cells with the same performance as flow cytometry. This makes the proposed method a promising alternative for diagnosing sepsis in decentralized healthcare schemes.

Optimized detection of lung IL-6 via enzymatic liquefaction of low respiratory tract samples: application for managing ventilated patients.

Lung IL-6 is a promising biomarker for predicting respiratory failure during pulmonary infections. This biomarker is found in respiratory samples which need to be liquefied prior to analysis. Traditional liquefying methods use reducing agents such as dithiothreitol (DTT). However, DTT impairs immunodetection and does not liquefy highly viscous samples. We propose an enzymatic method that liquefies samples by means of generating O2 bubbles with endogenous catalase. Low respiratory tract specimens from 48 mechanically ventilated patients (38 with SARS-CoV-2 infection) were treated with DTT or with the enzymatic method. We used turbidimetry to compare the liquefaction degree and IL-6 was quantified with ELISA. Finally, we used AUC-ROC, time-to-event and principal component analysis to evaluate the association between respiratory compromise or local inflammation and IL-6 determined with both methods. Enzymatically treated samples were better liquefied than those reduced by DTT, which resulted in higher ELISA signals. Lung IL-6 levels obtained with the enzymatic procedure were negatively correlated with the oxygenation index (PaO2/FiO2) and the time of mechanical ventilation. The proposed enzymatic liquefaction method improves the sensitivity for lung IL-6 detection in respiratory samples, which increases its predictive power as a biomarker for evaluating respiratory compliance.

Immunodetection of Lung IgG and IgM Antibodies against SARS-CoV-2 via Enzymatic Liquefaction of Respiratory Samples from COVID-19 Patients.

Lung-secreted IgG and IgM antibodies are valuable biomarkers for monitoring the local immune response against respiratory infections. These biomarkers are found in lower airway secretions that need to be liquefied prior to analysis. Traditional methods for sample liquefaction rely on reducing disulfide bonds, which may damage the structure of the biomarkers and hamper their immunodetection. Here, we propose an alternative enzymatic method that uses O2 bubbles generated by endogenous catalase enzymes in order to liquefy respiratory samples. The proposed method is more efficient for liquefying medium- and high-viscosity samples and does not fragment the antibodies. This prevents damage to antigen recognition domains and recognition sites for secondary antibodies that can decrease the signal of immunodetection techniques. The suitability of the enzymatic method for detecting antibodies in respiratory samples is demonstrated by detecting anti-SARS-CoV-2 IgG and IgM to viral N-protein with gold standard ELISA in bronchial aspirate specimens from a multicenter cohort of 44 COVID-19 patients. The enzymatic detection sharply increases the sensitivity toward IgG and IgM detection compared to the traditional approach based on liquefying samples with dithiothreitol. This improved performance could reveal new mechanisms of the early local immune response against respiratory infections that may have gone unnoticed with current sample treatment methods.

Paper biosensors for detecting elevated IL-6 levels in blood and respiratory samples from COVID-19 patients.

Decentralizing COVID-19 care reduces contagions and affords a better use of hospital resources. We introduce biosensors aimed at detecting severe cases of COVID-19 in decentralized healthcare settings. They consist of a paper immunosensor interfaced with a smartphone. The immunosensors have been designed to generate intense colorimetric signals when the sample contains ultralow concentrations of IL-6, which has been proposed as a prognosis biomarker of COVID-19. This is achieved by combining a paper-based signal amplification mechanism with polymer-filled reservoirs for dispensing antibody-decorated nanoparticles and a bespoken app for color quantification. With this design we achieved a low limit of detection (LOD) of 10-3 pg mL-1 and semi-quantitative measurements in a wide dynamic range between 10-3 and 102 pg mL-1 in PBS. The assay time is under 10 min. The low LOD allowed us to dilute blood samples and detect IL-6 with an LOD of 1.3 pg mL-1 and a dynamic range up to 102 pg mL-1. Following this protocol, we were able to stratify COVID-19 patients according to different blood levels of IL-6. We also report on the detection of IL-6 in respiratory samples (bronchial aspirate, BAS) from COVID-19 patients. The test could be easily adapted to detect other cytokines such as TNF-α and IL-8 by changing the antibodies decorating the nanoparticles accordingly. The ability of detecting cytokines in blood and respiratory samples paves the way for monitoring local inflammation in the lungs as well as systemic inflammation levels in the body.

Ultrafast and Ultrasensitive Naked-Eye Detection of Urease-Positive Bacteria with Plasmonic Nanosensors.

Identifying the pathogen responsible for an infection is a requirement in order to personalize antimicrobial treatments. Detecting bacterial enzymes, such as proteases, lipases, and oxidoreductases, is a winning approach for detecting pathogens at the point of care. In this Article, a new method for detecting urease-producing bacteria rapidly and at ultralow concentrations is reported. In this method, longsome bacteriological culture steps are substituted for a 10 min capture procedure with positively charged magnetic beads. The presence of urease-positive bacteria on the particles is then queried with a plasmonic signal generation step that generates blue- or red-colored nanoparticle suspensions upon addition of the enzyme substrate. These colorimetric signals, which can be easily identified by eye, are generated by the NH3-dependent assembly of gold nanoparticles in the presence of bovine serum albumin (BSA). The proposed method can detect Proteus mirabilis with a limit of detection of 101 cells mL-1, with a total assay time of 40 min, even in the presence of a large excess of urease-negative bacteria ( Pseudomonas aeruginosa). Furthermore, it does not require bulky equipment, and it can detect P. mirabilis at clinically relevant concentrations within minutes, making it suitable for detecting urease-positive pathogens at the point of care.