Citations

Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies. However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. The current therapy monitoring depends largely on subjective symptom assessment, routine laboratory tests, and basic vital signs, without real-time, quantitative evaluation of CAR T-cell expansion or activation in clinical practice. This lack of timely immune monitoring hampers individualized care and contributes to increased treatment costs. To address this need, we present a proof-of-concept, label-free rapid optical imaging (ROI) biosensor with automated machine learning analysis for direct quantification of CAR T-cells from whole blood. This microfluidic platform integrates red blood cell (RBC) removal, CAR T-cell capture, and imaging-based quantification on a single chip, eliminating the need for centrifugation, staining, and operator-dependent interpretation. For validation, 50 μL whole blood samples spiked with Jurkat cells expressing CD19 CARs underwent RBC depletion by agglutination and microfiltration. The remaining blood components were then incubated on a sensor chip functionalized with recombinant CD19 protein. Captured CAR T-cells were imaged by brightfield microscopy and automatically enumerated using a machine learning algorithm trained on fluorescence-validated cells. The CD-19 cells’ capture performance was validated by flow cytometry and fluorescence imaging. The trained machine learning model validated at 88% sensitivity and 96% specificity. Buffer and whole blood calibration curves were established across clinically relevant concentrations (1–1000 cells/µL) with triple replicates. The results showed high correlation (0.975 and 0.990 R2) between the spiked concentration and the detected CAR T-cells, with a 95% certainty limit of detection (LOD) and quantification (LOQ) of 0.6 and 1.1 cells/µL for spiked buffer, and 14 and 67 cells/µL for spiked whole-blood, respectively.

Ketones, which are key biomarkers of fat oxidation, are relevant for metabolic health maintenance and disease development, making continuous monitoring essential. In this study, we introduce a novel colorimetric sensor designed for potential continuous acetone detection in biological fluids. The sensor features a polydimethylsiloxane (PDMS) shell that encapsulates a sensitive and specific liquid-core acetone-sensing probe. The microsphere sensors were characterized by evaluating their size, PDMS shell thickness, colorimetric response, and sensitivity under realistic conditions, including 100% relative humidity (RH) and CO2 interference. The microsphere size and sensor sensitivity can be controlled by modifying the fabrication parameters. Critically, the sensor showed high selectivity for acetone detection, with negligible interference from CO2 concentrations up to 4%. In addition, the sensor displayed good reproducibility (CV < 5%) and stability under realistic storage conditions (over two weeks at 4 °C). Finally, the accuracy of the microsphere sensor was validated against a gold standard gas chromatography-mass spectrometry (GC-MS) method using simulated and real breath samples from healthy individuals and type 1 diabetes patients. The correlation between the microsphere sensor and GC-MS produced a linear fit with a slope of 0.948 and an adjusted R-squared value of 0.954. Therefore, the liquid-core microsphere-based sensor is a promising platform for acetone body fluid analysis.

Abstract Single‐molecule immunoassay is a reliable technique for the detection and quantification of low‐abundance blood biomarkers, which are essential for early disease diagnosis and biomedical research. However, current single‐molecule methods predominantly rely on endpoint detection and necessitate signal amplification via labeling, which brings a variety of unwanted effects, like matrix effect and autofluorescence interference. This study introduces a real‐time mass imaging‐based label‐free single‐molecule immunoassay (LFSMiA). Featuring plasmonic scattering microscopy‐based mass imaging, a 2‐step sandwich assay format enables background reduction, minimization of matrix effect by dynamic tracking of single binding events, and fully leveraging real‐time data for improved measurement precision through a Bayesian Gaussian process model, the LFSMiA enables ultra‐sensitive and direct protein detection at the single‐molecule level in neat blood sample matrices. LFSMiA measurement is demonstrated for interleukin‐6 and prostate‐specific antigen in buffer, undiluted serum, and whole blood with sub‐femtomolar detection limits and eight logs of dynamic ranges. Moreover, comparable performance is achieved with an inexpensive miniaturized setup. To show its translational potential to clinical settings and point‐of‐care diagnostics, N‐terminal pro‐B‐type natriuretic peptide is examined in patient whole blood samples using the LFSMiA and results in a strong linear correlation (r > 0.99) with standard clinical lab results.

Ketones are well-known biomarkers of fat oxidation produced in the liver as a result of lipolysis. These biomarkers include acetoacetic acid and β-hydroxybutyric acid in the blood/urine and acetone in our breath and skin. Monitoring ketone production in the body is essential for people who use caloric intake deficit to reduce body weight or use ketogenic diets for wellness or therapeutic treatments. Current methods to monitor ketones include urine dipsticks, capillary blood monitors, and breath analyzers. However, these existing methods have certain disadvantages that preclude them from being used more widely. In this work, we introduce a novel acetone sensor device that can detect acetone levels in breath and overcome the drawbacks of existing sensing approaches. The critical element of the device is a robust sensor with the capability to measure acetone using a complementary metal oxide semiconductor (CMOS) chip and convenient data analysis from a red, green, and blue deconvolution imaging approach. The acetone sensor device demonstrated sensitivity of detection in the micromolar-concentration range, selectivity for detection of acetone in breath, and a lifetime stability of at least one month. The sensor device utility was probed with real tests on breath samples using an established blood ketone reference method.

Single-molecule immunoassay is a reliable technique for the detection and quantification of low-abundance blood biomarkers, which are essential for early disease diagnosis and biomedical research. However, current single-molecule methods all require signal amplification via labelling, which brings a variety of unwanted consequences, such as matrix effects and autofluorescence interference. Here, we introduce a real-time mass imaging-based label-free single-molecule immunoassay (LFSM-immunoassay). Featuring plasmonic scattering microscopy-based real-time mass imaging, a 2-step sandwich assay format-enabled background reduction, and minimization of matrix effects by dynamic tracking of single binding events, the LFSM-immunoassay enables ultra-sensitive and direct protein detection at the single-molecule level in neat blood sample matrices. We demonstrated that the LFSM-immunoassay can measure sub-femtomolar levels of interleukin-6 and prostate-specific antigen in whole blood with 8 log dynamic range. To show its translational potential to clinical settings, we measured NT-proBNP (N-terminal pro-brain natriuretic peptide) in 28 patient serum samples using a 20 minute LFSM-immunoassay, and the results show a strong linear correlation (r > 0.99) with clinical lab reported values.

Abstract Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1–2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate Escherichia coli from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish E. coli from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.

Abstract With the increasing prevalence of antibiotic resistance, the need to develop antimicrobial susceptibility testing (AST) technologies is urgent. The current challenge has been to perform the antibiotic susceptibility testing in short time, directly with clinical samples, and with antibiotics over a broad dynamic range of clinically relevant concentrations. Here, a technology for point‐of‐care diagnosis of antimicrobial‐resistant bacteria in urinary tract infections, by imaging the clinical urine samples directly with an innovative large volume solution scattering imaging (LVSi) system and analyzing the image sequences with a single‐cell division tracking method is developed. The high sensitivity of single‐cell division tracking associated with large volume imaging enables rapid antibiotic susceptibility testing directly on the clinical urine samples. The results demonstrate direct detection of bacterial infections in 60 clinical urine samples with a 60 min LVSi video, and digital AST of 30 positive clinical samples with 100% categorical agreement with both the clinical culture results and the on‐site agar plating validation results. This technology provides opportunities for precise antibiotic prescription and proper treatment of the patient within a single clinic visit.