Biosensors and Bioelectronics

Nanopores enable single-molecule analysis by measuring current signals through nanoscale pores in either biological or solid-state membranes. Accurate detection of analyte fingerprints within the pore environment is essential for reading-out the analyte type. We develop a framework for robust and label-free detection of the molecular nanopore events using a graph representation of the measured signals. To this end, we build a graph-based two-stage workflow based on a convolutional and graph neural networks that first perform a fast screening of the nanopore events, followed by a deep validation of these. The learned model can thus efficiently and in an unsupervised manner select possible molecular signatures (the current blockades) in the full signal, denoise, validate, reconstruct these, and predict the morphology of unseen molecular events. We could show that the learned model can efficiently predict the correct event morphology for the same analyte within a 2.4-fold range of transmembrane voltage values not included in the training. The developed graph-based workflow is modular, generalizable, and provided that it is trained on a huge amount of different nanopore experiments has the potential to become a blueprint model for nanopore read-out. Such a read-out model would be able to identify subtle differences in molecules like proteins, as well as their conformational or folding states. The proposed framework is developed using experimental signals from DNA translocation through an aerolysin pore and demonstrates a unified approach linking unsupervised feature learning to raw-signal inference for single-molecule sensing.

Neurotransmitters in the gut play a vital role in human health and neuroscience, and their real-time monitoring is essential for understanding underlying physiological mechanisms. However, bioelectronic systems capable of measuring neurotransmitters in vivo at the anatomical site of interest remain underdeveloped and largely depend on bulky, off-the-shelf electronic components, thereby constraining the development of systems that are both practical and minimally invasive. Here, we report a miniature ingestible pill that is capable of real-time in vivo sensing of two key neurotransmitters: serotonin (5-HT) and dopamine (DA). The system incorporates a fully printed three-electrode-based electrochemical sensor for neurotransmitter sensing and a custom application-specific integrated circuit (ASIC) that integrates all major functional blocks on a single chip, enabling a platform for fully wireless monitoring of gut neurotransmitters. The pill, measuring 5.8 mm in diameter and 19 mm in length, supports multiple electrochemical sensing techniques, including amperometry and voltammetry, with only 42 A of average current consumption. We demonstrate the ingestible platform through in vivo studies in rat animal models, enabling real-time monitoring of gut neurotransmitters.

Over the past decades, the frequency of viral outbreaks has increased substantially worldwide, driven in part by global travel and resulting in millions of deaths each year. This trend underscores the urgent need for rapid, simple, and accessible diagnostic tools for infectious disease detection. Here, we present a nanofluidic digital chip (Nano-dChip) for point-of-care viral RNA detection that delivers results within 30 minutes at a cost of less than $0.50 per chip. The Nano-dChip employs reverse transcription loop-mediated isothermal amplification (RT-LAMP) for highly sensitive and specific target amplification. Reaction reagents are compartmentalized into numerous nanofluidic reservoirs, enabling highly quantitative detection while minimizing contamination risks. Using a single chip, we successfully detect both SARS-CoV-2 and Influenza H3 RNA with a detection limit of 10 fM, demonstrating the Nano-dChips potential as a rapid, low-cost, and scalable diagnostic platform for timely outbreak control.

Aptamers are attractive receptors for small-molecule biomarker detection in complex samples because of their high stability, affinity, and specificity, but aptamer-based sensors generally lack the sensitivity to detect low-abundance analytes. As a solution, we developed the charge-amplified FET (CAFET) aptamer biosensor, which is designed to amplify the net charge variation within the Debye length that occurs as a consequence of aptamer-target binding. Our sensor utilizes a strand-displacement aptamer switch, which releases an initially-hybridized displacement strand (DS) upon target binding and thus induces a measurable net charge variation within the Debye length that is amplified to a large FET current response as signal readout. This signal can be further enhanced by adding a charge label to the DS. As a consequence, our sensor can achieve far greater sensitivity than previously described aptamer-FET sensors, where the binding-induced local charge variation is modest. We demonstrate 3-hydroxykynurenine and progesterone detection with a picomolar limit of detection in undiluted human plasma--four orders of magnitude lower than the dissociation constant (KD) of the aptamer component. The CAFET sensor design is modular and should be adaptable for the detection of a wide range of clinically-informative low-abundance analytes in complex samples.

Biomarkers in sweat and saliva offer a promising avenue for non-invasive health monitoring. Electrochemical sensors have the potential to measure such biomarkers simultaneously. However, they are limited in discriminating individual biomarkers in mixtures, as redox potentials often overlap, resulting in current signatures that cannot be deconvoluted. This study focuses on differentiating biomarkers using orthogonal sensing materials combined with machine learning. We introduce a flexible electrochemical sensor array comprising carbon flower electrodes modified with poly(vinylidene fluoride) (PVDF) or poly(4-vinylpyridine) (P4VP) for the detection of estradiol (E2), ascorbic acid (AA), serotonin (5-HT), and melatonin (Mel). The two polymers act by altering the redox potential and current response of each biomarker, thereby enhancing signal diversity and enabling peak separation. Using multi-output regression models on 450 single and mixture measurements, the array accurately predicts concentrations (R2 = 0.95) over a wide dynamic range spanning nanomolar to micromolar levels. Polymer-resolved analysis reveals that PVDF-modifications enhance E2 and Mel detection, while P4VP-modifications improve AA and 5-HT quantification, highlighting the benefit of complementary orthogonal sensing electrodes. This finding is further supported by feature attribution analysis, which shows that the machine learning model relies on polymer-specific electrochemical signatures, directly linking improved performance to distinct polymer-analyte interactions. Overall, these results demonstrate that combining polymer-modified orthogonal electrodes with machine learning enables accurate, multiplexed sensing in complex mixtures, advancing selective detection strategies for future sensor platforms.

Interleukin-6 (IL-6) is a key pro-inflammatory cytokine closely associated with sepsis progression and septic cardiomyopathy (SCM), a severe clinical condition characterized by acute cardiac dysfunction and high mortality in critically ill patients. Rapid and sensitive monitoring of IL-6 in clinical biofluids is therefore crucial for early diagnosis, disease prognosis, and timely therapeutic intervention in emergency healthcare settings. Herein, we report a biofunctional, label-free electrochemical aptasensor based on a graphitic carbon nitride-incorporated chitosan hydrogel-modified gold screen-printed electrode (MCH/Apt-IL-6/g-C3N4@CS/Au-SPE) for ultrasensitive detection of IL-6 in clinical biofluids. The electroactive g-C3N4@CS hydrogel heterointerface was engineered via electrostatic interactions between the negatively charged surface functionalities of two-dimensional graphitic carbon nitride (g-C3N4) and the protonated amino groups (-NH3+) of chitosan (CS), yielding a porous, conductive, and biocompatible sensing matrix with enhanced aptamer immobilization and accelerated electron-transfer kinetics. Biocompatibility evaluation using MTT assay and confocal fluorescence imaging demonstrated that the hydrogel maintained excellent cellular compatibility at 5 mg/mL, preserving normal cytoskeletal organization, mitochondrial integrity, and nuclear morphology, while higher concentrations induced cellular stress responses. Under optimized experimental conditions, the developed aptasensor exhibited outstanding analytical performance with an ultrawide linear detection range from 1 fg/mL to 10 ng/mL, a high sensitivity of 2.162 A/[log10(ng/mL)] cm-2, a low detection limit of 0.460 pg/mL, and excellent linearity (R2 = 0.979). In addition, the sensor demonstrated remarkable selectivity toward common biological interferents, including ascorbic acid, cysteine, glucose, glycine, and urea, together with excellent reproducibility (RSD = 1.139%). Validation studies performed in spiked human serum samples further confirmed the reliability and practical applicability of the proposed sensing platform for rapid clinical analysis. Owing to its label-free detection strategy, disposable electrode format, high sensitivity, and favorable biocompatibility, the developed g-C3N4-hydrogel heterointerface-based aptasensor represents a promising next-generation platform for early septic cardiomyopathy diagnostics, inflammatory biomarker monitoring, and point-of-care electrochemical biosensing applications.

MicroRNAs (miRNAs) are ultra-short RNA molecules characterized by high sequence homology, frequent post-transcriptional modifications, and typically low abundance, particularly in circulating biofluids. These inherent biological features present substantial technical challenges for RT-qPCR- based quantification. Consequently, the development of miRNA RT-qPCR assays has required architectural adaptations at the reverse transcription (RT) stage to generate extended cDNA templates, thereby enabling effective downstream quantitative PCR amplification. One widely adopted approach involves the enzymatic addition of a poly(A) tail to the 3' end of miRNAs, followed by poly(T)-primed universal reverse transcription, which has gained broad acceptance due to its perceived sensitivity and simplified workflow. However, independent experimental evidence indicates that this architecture does not consistently provide the level of specificity required for reliable single-nucleotide (SN) discrimination, particularly when quantifying low-abundance circulating miRNA targets, as demonstrated in our previous study. An alternative strategy relies on miRNA-specific reverse transcription using stem-loop priming has been equally well accepted. When generically generated, this approach offers certain improved specificity, but its performance in resolving single-nucleotide differences remains limited. In this article, we employed precision engineering to maximize specificity for both reverse transcription and qPCR steps. By tailoring both primer design and reaction architecture to the specific sequence features of each miRNA, we enable robust single nucleotide discrimination among these ultra-short targets. Prototype of ten different miRNova assays quantifying miRNAs whose sequences are differed in various configurations were tested on synthetic miRNA targets. For miRNova assay validation, saliva samples were elite rugby players submitted to small RNA extraction, then RT-qPCR. Spike-in of synthetic targets was applied for each quantification point to characterized the sensitivity, specificity and accuracy of the assays. Comparative analysis was performed between miRNova and two commercially available kits on the same sample set. The obtained results show a superior performance of miRNova assays allowing for sensitive and accurate quantification of miRNAs in saliva samples. Altogether, this results in modular, reproducible assays optimized for low-abundance miRNA detection in challenging biofluids, including saliva, positioning the platform beyond existing sensitivity-focused solutions toward true diagnostic-grade specificity.

Infections caused by multi-drug-resistant organisms (MDROs) pose a significant public health threat, responsible for over 2 million hospitalizations and 23,000 deaths annually in the United States. Microbiome dysbiosis (imbalance) is considered one of the main causes for MDRO colonization and the resulting infections. Rapid detection and intervention of MDRO outbreaks are crucial to alleviating strain on patients and healthcare facilities. Current diagnostic methods for MDRO detection are too slow and costly to provide the rapid MDRO detection necessary for patient care facilities. Here we present a rapid, accurate and cost-effective electrochemical sensor capable of MDRO detection down to [~]104 colony forming units (CFU)/g in mice and human stool samples. Our novel sensor utilizes probe-modified Screen-Printed Electrodes (SPEs) capable of hybridizing target gene sequences associated with MDROs. The resulting probe/target complex generates a unique and highly sensitive signal detectable down to 10 atto molar or 10 CFU/mL of target TEM-1 gene. The use of these pre-functionalized SPEs reduces individual sample analysis time to less than an hour. Several target sequences from two chromosomal target genes (AmpC and AcrB found in E. coli) have been identified and successfully detected in clinical stool samples with results comparable to the standard quantitative PCR method. Additional target genes associated with antibiotic resistance (TEM-1, VanA, KPC and SHV) have also been successfully detected in vitro and are ready for clinical evaluation. Future development includes multiplexing the sensor to simultaneously detect up to three MDROs target genes, including {beta}-lactamases that hydrolyze {beta}-lactams, the most commonly used antibiotics in clinical settings. This novel sensor platform will be a rapid, economical, point-of-care device with little requirement of reagent handling or technical training.

Rapid and highly selective sensing of ultra-low concentration protein biomarkers remains a critical challenge important for early disease diagnosis and monitoring. Here, we use conical SiO2 nanopore-based biosensing for the rapid detection of heart-type fatty acid binding protein (H-FABP). Antibodies were covalently immobilized on the nanopore surface through siloxane chemistry. The functionalized asymmetric nanopores generate a characteristic rectifying current-voltage response, which shows a distinct shift upon binding to the target protein due to partial neutralization of the negatively charged pore surface. The sensor exhibits excellent sensitivity in the attomolar to nanomolar concentration range with a detection limit (LOD) of [~]0.4 aM. Furthermore, the platform exhibits high selectivity, distinguishing H-FABP from non-target proteins (HSA and Hb) at concentrations six orders of magnitude higher. We also demonstrate that nanopores can be regenerated using sodium hypochloride and O2 plasma treatment, enabling repeated functionalization and reuse.

Extracellular vesicles are increasingly recognized as important carriers of disease-associated molecular information, yet robust methods for their isolation and molecular characterization from limited clinical samples remain challenging. Here, we present an integrated approach combining standardized EV isolation, label-free Surface-Enhanced Raman Spectroscopy (SERS), and artificial intelligence (AI) for comprehensive molecular profiling of small extracellular vesicles (sEVs) from human plasma. Here, we show systematically isolated and characterized plasma sEVs using ExoTIC in accordance with MISEV2023 guidelines, with SERS analysis revealing quantifiable spectral differences across samples from patients with glioblastoma (n=20) and meningioma (n=23) compared to healthy controls (n=30). Among the evaluated AI models, the convolutional neural network most effectively captured group-level spectral differences in sEVs, achieving accuracies up to 88% in this pilot cohort. Further, an EGFR-based spectral regression model was explored to examine molecular variability across sEV samples. Parallel proteomic analysis presented statistically significant differences in several proteins elevated in glioblastoma or meningioma. This label-free, rapid approach provides a proof-of-concept framework for sEV molecular profiling establishing the basis for broad validation studies across diverse diseases.

Fluorogenic aptamers (FAPs) are emerging molecular probes for viral RNA and DNA sensing. However, their use in multiplexed nucleic acid sensing has been hindered by cross-reactivity and overlapping emission spectra. Here we address these limitations by introducing a fluorescence-lifetime-based multiplexed detection strategy using variants of the DNA fluorogenic aptamer Lettuce that exhibits distinct fluorescence lifetimes when complexed with the fluorogen TO1-biotin. To effectively evolve Lettuce for diverse lifetimes, we developed a large-scale screening platform, termed FAP-FLIM-NGS (fluorogenic aptamer-based fluorescence lifetime imaging microscopy on next-generation sequencing chips), which measures the fluorescence lifetimes of [~]104 Lettuce/TO1-biotin complexes directly on an Illumina MiSeq flow cell. Using this approach, three variants with markedly different lifetimes were identified: a single mutant (smC14T, 6.0 ns) and two double mutants (dmA5T/C14T, 5.2 ns, and dmA5T/T22A, 4.4 ns). To demonstrate the utility of these Lettuce variants in multiplexed detection, a set of split Lettuce probes targeting viral RNA fragments derived from SARS-CoV-2, MERS-CoV, and influenza A were designed and tested. Phasor plot analysis confirmed that these probes can robustly distinguish individual targets as well as mixtures containing any two or all three targets purely based on distinct fluorescence lifetimes of probes, thereby overcoming the challenges of cross-reactivity and spectral overlap. Beyond this proof of concept, our findings establish a generalizable strategy for engineering FAPs with customized photophysical properties, opening new avenues for next-generation diagnostics and molecular sensing technologies.

Extracellular vesicles (EVs) carry molecular signatures of their originating cells and have thus emerged as promising biomarkers. However, their clinical utility remains limited due to their low abundance and the modest sensitivity of current EV detection methods in complex biological environments. Here, we present a quantum well defect functionalized carbon nanotube sensor coupled with integrin-recognition RGD tripeptide for EV detection in human plasma. Leveraging the abundance of integrins on EV surfaces, we targeted 5{beta}1, V{beta}1, and V{beta}3 subtypes. The nanosensor exhibited robust hypsochromic shifts in defect emission upon integrin binding, achieving sub-picomolar detection limits for integrin subunits and quantifying EVs at concentrations as low as 104 EVs{middle dot}mL-1 for glioblastoma, ovarian cancer, and fibroblast cell-derived EV types. Molecular dynamics simulation indicated that integrin docking at the RGD-coupled quantum defect can substantially reshape the interfacial environments of the quantum defects, explaining the high sensitivity in EV detection in complex biological media. Finally, transmembrane protein analysis validated the expression of surface integrins across the tested EV types. The modular nanosensor construct can be targeted to detect disease-associated EV subpopulations, advancing EV-based diagnostics.

Recent advances in plasmonic biosensing and imaging have enabled label-free analysis of single biological nanoparticles. We previously developed PlAsmonic NanOapeRture lAbel-free iMAging (PANORAMA) for isolation and purification-free, digital counting and precise localization of small extracellular vesicles (sEVs), with complementary fluorescence interrogation of surface and intravesicular biomarkers for quantitative molecular profiling. The fact that no isolation and purification or isolation is needed represents a crucial advantage because various specificity, efficiency, and time-consumption issues hinder quantitatively reproducible extraction of sEVs from biological fluids. PANORAMA achieves ultrahigh refractive-index sensitivity through arrayed gold nanodisks on invisible substrates (AGNIS) fabricated by nanosphere lithography (NSL). However, despite its simplicity and low cost, NSL is frequently constrained by poor large-area uniformity, which hinders scalable fabrication. Here, we introduce nanosphere settling lithography (NSSL) as an alternative to the gold-standard Langmuir-Blodgett trough (LBT) process, enabling highly uniform, large-area monolayers with reduced process stringency. AGNIS fabricated via NSSL exhibits high refractive-index sensitivity with low spatial variability across 60 mm x 24 mm substrates, sufficient for 60-well in standard 384-well plate format. The platform demonstrates exquisite sensitivity through PANORAMA digital counting and sizing of 25, 50, and 100 nm polystyrene beads, as well as single-vesicle characterization of sEVs derived from H460 lung cancer cells. For the first time, combined PANORAMA and fluorescence imaging enables quantitative analysis of microRNA-21 (miR-21) expression in sEVs to identify "cancer-suspicious" sub-population from liver cancer patient plasma in an unbiased fashion allowing both highly sensitive detection of individual sEVs and simultaneous molecular profiling. Collectively, NSSL enables uniform, high-performance plasmonic biosensing over large areas, providing a scalable and economical pathway for high-throughput, digital single-sEV analysis and translational liquid biopsy applications.

Anthracycline chemotherapeutics are commonly used as frontline treatments for a wide array of cancers. However, their administration to patients results in substantial side effects, primarily cardiotoxicity, as well as myelosuppression and gastrointestinal toxicity. Current clinical management of such side effects is solely based on a lifetime dosage limit, which inhibits their anti-tumor efficacy. Many individualized factors, including age, family history of cardiovascular disease, treatment regimen, and other co-morbidities influence drug pharmacology. Despite this heterogeneity, there is no method for determining actual organ or tumor exposure to the treatment in an individual. Here, we developed an optical nanosensor array for four anthracyclines--doxorubicin, daunorubicin, epirubicin, and idarubicin. We used single-walled carbon nanotubes as the signal transducer due to their tunable near-infrared fluorescence. We screened twelve distinct ssDNA sequences paired with seven SWCNT (n,m) species at increasing concentrations of each of the four anthracyclines. The spectral responses were then used to develop machine learning-based classification models to identify different anthracycline types and concentrations. The optimized extreme gradient boosting model was able to classify high levels of each anthracycline with 100% accuracy. Concentration-based classification by PCA was performed for each anthracycline, distinguishing low ([≤] 5 {micro}M) and high (> 5 {micro}M) concentrations. Finally, we validated the sensor performance using synthetic urine and sweat. Our findings demonstrate the potential of carbon nanotube-based sensor array to measure the pharmacokinetics of anthracyclines in patients with the goal of enhancing anti-tumor efficacy and monitoring off-target toxicities.

Cancer is a significant contributor to global mortality and places a substantial burden on healthcare systems, underscoring the need for improved strategies for developing and evaluating new therapies. Electrochemical impedance monitoring of in vitro cancer models is a promising technique for evaluating treatment effectiveness, particularly for evaluating how well a drug may kill cancer cells. This approach is advantageous over conventional end-point assays because it is non-destructive, label-free, and can provide temporal information on cell behavior and drug kinetics. However, traditional impedance devices are limited in that they do not support three-dimensional cell culture that has become standard in cancer studies. Typical devices are planar substrates that support monolayer culture, which has been shown to overestimate drug effectiveness. In this work, we propose 3D printed bioelectronic scaffold devices that provide 3D cancer cell culture while functioning as an on-chip readout for monitoring changes in cell characteristics via impedance. We describe device development and demonstrate reproducible fabrication, stable electrochemical properties, cell detection by impedance, and proof-of-concept monitoring of cytotoxicity in response to a chemotherapeutic drug. Overall, this technology offers a promising platform that could be further developed for compound screening as part of drug development or precision medicine.

Continuous monitoring of protein biomarkers could transform the management of acute and chronic diseases. Despite tremendous potential, wearable health monitors have remained largely limited to metabolites and small molecules. A key challenge is the limited availability of biointerfaces that reversibly track low-abundance proteins in vivo without user intervention. Here, we present the Differential Aptalyzer, a minimally invasive hydrogel microneedle platform for continuous monitoring of proteins in skin interstitial fluid. The platform combines high-affinity antibodies for selective target capture with aptamers for reversible electrochemical signal transduction. When integrated into a differential electrochemical chip and pulse-assisted sensor regeneration, this approach enables continuous monitoring of proteins in a wearable format. Using cardiac troponin I (cTnI) as a clinically-relevant model analyte, Differential Aptalyzer offers a broad dynamic range (0.003-0.640 ng/mL) and strong specificity against interfering proteins. Importantly, this platform reliably tracks both rising and falling exogenous cTnI levels injected into healthy mice, as well as endogenously elevated cTnI in a double-knockout mouse model of coronary artery disease, demonstrating its capability in continuous protein monitoring and identifying coronary artery disease cohorts.

SignificanceAbnormalities in potassium ion concentrations across subregions of the hippocampus have been implicated in seizures and other pathologies. Direct measurements of potassium ion concentrations are largely made using invasive electrodes, which do not allow for wide spatial coverage. This fluorescent nanoparticle potassium sensor enables direct visualization of potassium dynamics and represents a minimally invasive alternative to electrode-based methods. AimHere, we present a DNA-based fluorescence nanoprobe capable of sensing relative concentrations of potassium ions within populations of neurons. We present its effectiveness in monitoring neuronal K+ dynamics in response to electrical stimulation ex vivo. ApproachWe used widefield fluorescence microscopy to monitor changes in fluorescence intensity in labeled brain tissue in response to electrical stimulation ex vivo. ResultsWe found that our nanoprobe could be retained within the intracellular compartment and modulate in fluorescence intensity linearly in response to induced electrical current. Our K+ Sensor showed a fractional fluorescence change of approximately 1% per 10 mA of applied stimulation current in brain tissue. Optical spectroscopy confirmed the selectivity of the nanoprobes to potassium ions over other endogenous ions. ConclusionsOur findings indicate that this nanoprobe can be used to detect more complex potassium dynamics implicated in various pathologies of the nervous system, such as migraines, seizures, and trauma.

In todays world, point-of-care nucleic acid detection still remains extensively constrained and limited by the heavy dependence on centralized urban instrumentation facilities and complex assay workflows. Here, we elucidate a glucometer-based analytical platform that enables label-free detection of nucleic acids and the nucleic acid amplification products through a simple redox-mediated mechanism. The approach leverages the potassium ferricyanide (K3[Fe(CN)6])/ potassium ferrocyanide (K4[Fe(CN)6]), redox system, which is intrinsic to commercial glucometers, complementing with interactions between methylene blue (MB) and nucleic acids. These interactions transduce concentration differences in nucleic acids into quantifiable electrochemical signal readouts. Distinct varied signal outputs are observed between single-stranded and double-stranded DNA, enabling the direct detection as well as integration with nucleic acid amplification tests (NAATs), including polymerase chain reaction, rolling circle amplification, and loop-mediated isothermal amplification. Optimization of reaction parameters and conditions leads to enhancement of the overall signal discrimination and sensitivity across various assay formats. This innovation repurposes widely available off-the-shelf glucometers as a low-cost, portable nucleic acid detectors, thus eliminating the need for any specialized instrumentation. Our results enumerate and establish a generalized and scalable strategy for nucleic acid sensing. The platform thus supports sustainable and environmentally responsible point-of-care testing, thereby enabling improved accessibility and public health monitoring at resource-limited and remote settings.

Quantitative assessment of cellular oxidative stress requires simultaneous measurement of intracellular redox state and extracellular respiratory activity, yet integrated sensing approaches remain limited. Here, we present a dual-fluorescent sensing platform combining a genetically encoded redox biosensor (roGFP2{square}Tsa2{Delta}CR) with an optical oxygen sensor embedded in microwell plates for parallel, non-invasive quantification of intracellular reactive oxygen species (ROS) and oxygen consumption rates (OCR) in industrial yeast systems. The roGFP2-based sensor was stably expressed in Saccharomyces cerevisiae (S. cerevisiae) and Yarrowia lipolytica (Y. lipolytica), enabling dynamic monitoring of oxidative stress at population, single-cell, and subcellular levels, while oxygen-sensitive films provided real-time respiration measurements. Using this platform, we identified distinct redox-respiration phenotypes between the two yeasts. Crabtree-positive S. cerevisiae exhibited low OCR and mitochondrial ROS during glucose cultivation, whereas growth on glycerol increased OCR and mitochondrial ROS by ~2.5-fold and 12%, respectively. In contrast, the obligate respiratory yeast Y. lipolytica displayed 3-fold higher OCR and 16% lower mitochondrial ROS than respiring S. cerevisiae, indicating differences in respiratory oxidative burden. Antimycin A treatment reduced OCR by 60% in respiring S. cerevisiae while increasing mitochondrial ROS by 35%, whereas Y. lipolytica showed greater resistance to respiratory and oxidative perturbations. By integrating intracellular redox sensing with extracellular oxygen measurements, this platform enables quantitative coupling of redox state and respiration in living cells. The approach provides a scalable framework for evaluating cellular fitness, stress tolerance, and metabolic state in biomanufacturing and synthetic biology.

While high-dimensional flow cytometry plays critical roles in resolving complex cellular networks, there remains a scarcity of comprehensive panels for the simultaneous profiling of diverse mouse cell types, primarily due to the inherent difficulty of multiplexing. To address this technical gap and resolve diverse cell populations in murine models, we designed a 27-color flow cytometry panel optimized for 3-laser spectral flow cytometers. This optimized panel enables broad and simultaneous detection of 16 distinct cell subsets from both lymphoid and myeloid lineages--including T cells, B cells, plasma cells, NK cells, innate lymphoid cells, dendritic cells, monocytes, macrophages, neutrophils, eosinophils, basophils, mast cells--along with non-immune cells, such as epithelial, endothelial, fibroblast, and neuronal cells. The panel has been successfully applied to various tissues, including spleen, thymus, bone marrow, peripheral blood, mesenteric lymph nodes, peritoneal lavage fluid, gut epithelium, and lamina propria. Applying this panel to a poly(I:C) model, we successfully tracked dynamic shifts in monocyte and neutrophil populations and identified a previously unrecognized, glucocorticoid-producing cell subset via reporter expression. This panel will facilitate high-dimensional immune profiling on standard 3-laser cytometers, providing a robust tool for dissecting cellular dynamics across diverse contexts.