Peter B. Lillehoj

The diagnosis of chronic Chagas disease is challenging due to the wide genetic diversity of Trypanosoma cruzi, the causative agent of Chagas disease, and low levels of parasitemia, resulting in low sensitivity and accuracy with existing diagnostics. We report a magneto-immunoassay that employs dually labeled magnetic beads incorporating a recombinant Tc24 antigen, which is homologous across multiple discrete typing units of T cruzi. In this pilot study, 102 serum samples from 7 endemic countries were tested by this magneto-immunoassay, revealing its ability to distinguish Chagas-positive from Chagas-negative cases more accurately and faster than a standard Tc24-based immunoassay.

Abstract The quantification of immune cell subpopulations in blood is important for the diagnosis, prognosis and management of various diseases and medical conditions. Flow cytometry is currently the gold standard technique for cell quantification; however, it is laborious, time-consuming and relies on bulky/expensive instrumentation, limiting its use to laboratories in high-resource settings. Microfluidic cytometers offering enhanced portability have been developed that are capable of rapid cell quantification; however, these platforms involve tedious sample preparation and processing protocols and/or require the use of specialized/expensive instrumentation for flow control and cell detection. Here, we report an artificial intelligence-enabled microfluidic cytometer for rapid CD4 + T cell quantification in whole blood requiring minimal sample preparation and instrumentation. CD4 + T cells in blood are labeled with anti-CD4 antibody-coated microbeads, which are driven through a microfluidic chip via gravity-driven slug flow, enabling pump-free operation. A video of the sample flowing in the chip is recorded using a microscope camera, which is analyzed using a convolutional neural network-based model that is trained to detect bead-labeled cells in the blood flow. The functionality of this platform was evaluated by analyzing fingerprick blood samples obtained from healthy donors, which revealed its ability to quantify CD4 + T cells with similar accuracy as flow cytometry (<10% deviation between both methods) while being at least 4× faster, less expensive, and simpler to operate. We envision that this platform can be readily modified to quantify other cell subpopulations in blood by using beads coated with different antibodies, making it a promising tool for performing cell count measurements outside of laboratories and in low-resource settings.

demonstrations showcase the capability of this technique in generating polymer microwell arrays for high-throughput cellular studies, including cell growth dynamics studies and cell interaction studies. Furthermore, we envision that these platforms can be used with different cell types and for other biological applications, such as spheroid formation and single cell analysis, further expanding the utility of this technique.

CRISPR-Cas-based lateral flow assays (LFAs) have emerged as a promising diagnostic tool for ultrasensitive detection of nucleic acids, offering improved speed, simplicity and cost-effectiveness compared to polymerase chain reaction (PCR)-based assays. However, visual interpretation of CRISPR-Cas-based LFA test results is prone to human error, potentially leading to false-positive or false-negative outcomes when analyzing test/control lines. To address this limitation, we have developed two neural network models: one based on a fully convolutional neural network and the other on a lightweight mobile-optimized neural network for automated interpretation of CRISPR-Cas-based LFA test results. To demonstrate proof of concept, these models were applied to interpret results from a CRISPR-Cas13-based LFA for the detection of the SARS-CoV-2 N gene, a key marker for COVID-19 infection. The models were trained, evaluated, and validated using smartphone-captured images of LFA devices in various orientations with different backgrounds, lighting conditions, and image qualities. A total of 3146 images (1569 negative, 1577 positive) captured using an iPhone 13 or Samsung Galaxy A52 Android smartphone were analyzed using the trained models, which classified the LFA results within 0.2 s with 96.5% accuracy compared to the ground truth. These results demonstrate the potential of machine learning to accurately interpret test results of CRISPR-Cas-based LFAs using smartphone-captured images in real-world settings, enabling the practical use of CRISPR-Cas-based diagnostic tools for self- and at-home testing.

Interstitial fluid (ISF) is a promising diagnostic sample due to its extensive biomolecular content while being safer and less invasive to collect than blood. However, existing ISF sampling methods are time-consuming, require specialized equipment, and yield small amounts of fluid (<5 μL). We have recently reported a simple and minimally invasive technique for rapidly sampling larger quantities of dermal ISF using a microneedle (MN) array to generate micropores in the skin from which ISF is extracted using a vacuum-assisted skin patch. Here, we present step-by-step protocols for fabricating the MN array and skin patch, as well as for using them to sample ISF from human skin. Using this technique, an average of 20.8 μL of dermal ISF can be collected within 25 min, which is a ∼6-fold improvement over existing ISF sampling methods. Furthermore, the technique is well-tolerated and does not require the use of expensive or specialized equipment. The ability to collect ample volumes of ISF in a quick and minimally invasive manner will facilitate the analysis of ISF for biomarker discovery and its use for diagnostic testing. Key features • Minimally invasive (bloodless and nearly painless) technique for sampling ISF from human skin. • An average of 20.8 μL of interstitial fluid can be collected within 25 min. • This technique does not require expensive or specialized equipment or electricity. • Collected ISF can be analyzed using conventional laboratory-based assays or point-of-care diagnostic tests.

<h3>Background</h3> Tumor antigen-specific CD8+ T cells offer a compelling method for therapeutic targeting of solid tumors. However, efforts to identify tumor antigen-specific T cells have been hampered by limitations such as the need to pre-select antigens, inherently biasing such analyses. Here, we propose a method called ATTACH (Assessment of T cells Tethered to Antigen Class I/II Histocompatibility) to enrich for tumor antigen-specific T cells using tumor cells as <i>de facto</i> ‘tetramer pools’, thereby allowing for rapid, versatile, and unbiased isolation of antigen-specific T cells. <h3>Methods</h3> Lewis lung carcinoma cells expressing the ovalbumin antigen (LLC-OVA) were seeded overnight into microfluidic slides. The following day, OT-I alone or in combination with C57BL/6 CD8 splenocytes were added and a low, constant rate of fluidic shear stress was applied to remove nonspecific/weakly bound T cells. Fluorescent microscopy was used to quantify the retention of T cells followed by recovery for downstream functional analyses. Experiments were repeated using human antigens and TCR-engineered cells. <h3>Results</h3> Enumeration of fluorescent cells pre- and post-ATTACH confirmed a 5-fold antigen-dependent enrichment of OT-I in presence versus absence of OVA (23.25% vs 4.36%, <i>p=</i>0.0004). CD8 co-receptor blockade confirmed retention was mediated through TCR/pMHC binding (8.79% vs 2.86% <i>p</i>=0.075). With a 1:1 ratio of OT-I to C57BL/6 CD8⁺ T cells, a 2-fold enrichment of antigen-specific T cells (16.40% vs. 7.7%, <i>p=0.0151</i>) was achieved. Furthermore, enriched antigen-specific populations recovered using ATTACH exhibited a 12-fold increase in IFN-g secretion (<i>p=0.022</i>) and 3-fold higher cytotoxic potential as measured by cleaved caspase-3/7. Moreover, experiments using human TCR-engineered T cells showed a 5-fold enrichment for antigen-specific T cells (25.89% vs 5.48%, <i>p=0.058</i>). <h3>Conclusions</h3> Here, we demonstrate the feasibility of enriching for antigen-specific T cells recognizing solid tumor antigens using a microfluidic platform. Additional platform optimization and validation experiments are underway.

Rapid diagnostic tests (RDTs) offer valuable diagnostic information in a quick, easy-to-use and low-cost format. While RDTs are one of the most commonly used tools for in vitro diagnostic testing, they require the collection of a blood sample, which is painful, poses risks of infection and can lead to complications. We introduce a blood-free point-of-care diagnostic test for the rapid detection of protein biomarkers in dermal interstitial fluid (ISF). This device consists of a lateral flow immunochromatographic assay (LFIA) integrated within a microfluidic skin patch. ISF is collected from the skin using a microneedle array and vacuum-assisted extraction system integrated in the patch, and transported through the lateral flow strip via surface tension. Using this skin patch platform, we demonstrate in situ detection of anti-tetanus toxoid IgG and SARS-CoV-2 neutralizing antibodies, which could be accurately detected in human ISF in <20 min. We envision that this device can be readily modified to detect other protein biomarkers in dermal ISF, making it a promising tool for rapid diagnostic testing.

We report the development of highly wicking cellulose microneedles (MNs) for the rapid extraction and transport of dermal interstitial fluid (ISF).The MN array is made using a unique fabrication process, resulting in mechanically robust microneedles with exceptional wicking properties.For proof of concept, the cellulose MN array was integrated with a lateral flow immunochromatographic assay (LFIA) and tested on an artificial skin model, revealing its ability to autonomously extract fluid for subsequent LFIA-based biomarker detection.

Interstitial fluid (ISF) contains a wealth of biomolecules, yet it is underutilized for diagnostic testing due to a lack of rapid and simple techniques for collecting abundant amounts of fluid. Here, we report a simple and minimally invasive technique for rapidly sampling larger quantities of ISF from human skin. A microneedle array is used to generate micropores in skin from which ISF is extracted using a vacuum-assisted skin patch. Using this technique, an average of 20.8 μL of dermal ISF is collected in 25 min, which is an ∼6-fold improvement over existing sampling methods. Proteomic analysis of collected ISF reveals that it has nearly identical protein composition as blood, and >600 medically relevant biomarkers are identified. Toward this end, we demonstrate the detection of SARS-CoV-2 neutralizing antibodies in ISF collected from COVID-19 vaccinees using two commercial immunoassays, showcasing the utility of this technique for diagnostic testing.

The detection and quantification of protein biomarkers in bodily fluids is important for many clinical applications, including disease diagnosis and health monitoring. Current techniques for ultrasensitive protein detection, such as enzyme-linked immunosorbent assay (ELISA) and electrochemical sensing, involve long incubation times (1.5-3 hr) and rely on single-use sensing electrodes which can be costly and generate excessive waste. This work demonstrates a reusable electrochemical immunosensor employing magnetic nanoparticles (MNPs) and dually labeled gold nanoparticles (AuNPs) for ultrasensitive measurements of protein biomarkers. As proof of concept, this platform was used to detect C-X-C motif chemokine ligand 9 (CXCL9), a biomarker associated with kidney transplant rejection, immune nephritis from checkpoint inhibitor therapy, and drug-associated acute interstitial nephritis, in human urine. The sensor successfully detected CXCL9 at concentrations as low as 27 pg/mL within ~1 hr. This immunosensor was also adapted onto a handheld smartphone-based diagnostic device and used for measurements of CXCL9, which exhibited a lower limit of detection of 65 pg/mL. Lastly, we demonstrate that the sensing electrodes can be reused for at least 100 measurements with a negligible loss in analytical performance, reducing the costs and waste associated with electrochemical sensing.

Malaria is a major cause of illness and death worldwide. Rapid diagnostic tests are the most widely used tool for detecting malaria infection, however, they only provide binary results and lack the sensitivity needed to detect many asymptomatic infections. Molecular assays for quantifying malaria biomarkers offer higher detection sensitivity, however, they are time-consuming, and require expert training and expensive equipment, making them unsuitable for use in most of Africa. To address the need for simple, accurate and field-deployable malaria diagnostic tests, we have developed a microfluidic point-of-care (mPOC) immunoassay for rapid quantification of Plasmodium falciparum histidine-rich protein 2 (PfHRP2), a malaria parasite biomarker, in whole blood. This device features two diagnostic modes for detecting PfHRP2 at low (100's pg/mL) and high (1,000's ng/mL) concentrations, making it useful for multiple diagnostic applications, including the detection of asymptomatic infection, prediction of disease outcomes and diagnosis of cerebral malaria. Measurements of PfHRP2 in blood samples from malaria patients demonstrates that this platform offers similar accuracy as an ultra-sensitive PfHRP2 enzyme-linked immunosorbent assay (ELISA) test, while being 12× faster and simpler to use. This mPOC immunoassay can be deployed in rural health centers to assist clinicians in diagnosing and triaging malaria patients, ultimately improving patient outcomes.

This paper presents an innovative approach for flow-based quantification of immune cells in whole blood using microfluidics and machine learning. Target immune cells were labeled with antibody-coated microbeads and flowed inside a microfluidic device, and a convolutional neural network (CNN)-based object detection algorithm was utilized for the detection of bead-labeled cells. The detection range of this platform was evaluated by analyzing blood samples spiked with 10 µm-diameter polystyrene beads, which could be accurately quantified over a wide range of concentrations from 300 to 3,500 beads/µL. Proof-of-concept was demonstrated by quantifying CD4<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> T cells in three blood samples from human volunteers, which offered similar accuracy as cell counts determined by flow cytometry while being at least 1.5-fold faster and simpler to perform.

This paper presents a microfluidic device for the rapid identification of high-affinity anti-tumor T cells. This device consists of a polymethyl methacrylate (PMMA) microchannel containing a dense array of circular microwells fabricated using a unique CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> laser etching process. Using this device, OT-I T cells and LLC-OVA tumor cells were incubated within microwells to identify high-affinity T cells in <1 hour, which is significantly faster than existing T cell screening methods (requiring weeks/months) while being simpler to operate without requiring sophisticated instrumentation. Furthermore, this method is compatible with adherent and nonadherent tumor cells, making it a versatile platform for T cell-tumor cell interaction studies and T cell screening.

This paper presents a wearable skin patch for wireless measurements of protein biomarkers in dermal interstitial fluid (ISF). ISF is extracted from the skin using a microneedle (MN)-based, vacuum-assisted technique and autonomously transported through the patch via vacuum pressure. This device was used for quantitative measurements of C-X-C motif chemokine ligand 9 (CXCL9), a biomarker for autoimmune diseases and inflammation, which could be detected from 10 pg/mL to 1,000 pg/mL in phosphate-buffered saline (PBS) with a lower limit of detection of 1.33 pg/mL. Proof-of-concept was demonstrated by performing measurements on cadaver porcine skin dermally injected with ISF simulant spiked with CXCL9, which could be detected at 100 and 1,000 pg/mL, thus validating the functionality of this wearable sensor.

in <25 min. The ability to rapidly detect protein biomarkers with high sensitivity in a point-of-care format makes this device a promising tool for diagnostic testing, particularly in resource-limited settings.

In this paper, we present mathematical and computational analysis of a wearable impedance sensor that can be used for wireless measurement of analytes in bodily fluids. This sensor consists of an interdigitated electrode capacitor as the sensing element and a loop inductor as the antenna. The sensor’s resonance frequency was used to evaluate its response during different measurement conditions for mathematical analysis. Computational simulations of the sensor were also performed and the acquired values were compared with the analytical results, which were accurate within 10.8%. Experimental measurements of PfHRP2, a pathogen-specific protein biomarker, spiked in PBS were performed using a wireless impedance sensor prototype, which matched closely with the simulation results. These results demonstrate that the presented mathematical and computational analyses can accurately predict the response of the wireless impedance sensor, making them useful tools for designing wireless impedance sensors for wearable biosensing applications.

The detection and/or quantification of biomarkers in blood is important for the early detection, diagnosis, and treatment of a variety of diseases and medical conditions. Among the different types of sensors for detecting molecular biomarkers, such as proteins, nucleic acids, and small-molecule drugs, affinity-based electrochemical sensors offer the advantages of high analytical sensitivity and specificity, fast detection times, simple operation, and portability. However, biomolecular detection in whole blood is challenging due to its highly complex matrix, necessitating sample purification (i.e., centrifugation), which involves the use of bulky, expensive equipment and tedious sample-handling procedures. To address these challenges, various strategies have been employed, such as purifying the blood sample directly on the sensor, employing micro-/nanoparticles to enhance the detection signal, and coating the electrode surface with blocking agents to reduce nonspecific binding, to improve the analytical performance of affinity-based electrochemical sensors without requiring sample pre-processing steps or laboratory equipment. In this article, we present an overview of affinity-based electrochemical sensor technologies that employ these strategies for biomolecular detection in whole blood.