Loza F. Tadesse

Abstract T cell exhaustion impairs immune control of chronic diseases including tuberculosis, HIV, malaria, and cancer. Its clinical implications are vast, predicting HIV-associated malignancy and treatment response and limiting the efficacy of cell therapies. Despite the advantages of monitoring and removing exhausted T cells, current detection methods require expensive antibody labeling, destructive workflows, or days-long functional assays. Here, we introduce Raman spectroscopy as a label-free assay for distinguishing T cell states directly from culture while preserving viability for downstream use. We achieve >97% accuracy in discriminating unstimulated, activated, and exhausted T cells across three donors and multiple hardware setups. We identify vibrational modes associated with nucleic acid turnover and lipid remodeling as key features that distinguish T cell activation and exhaustion. In heterogeneous populations, we quantify exhaustion percentage with R 2 = 1 and strong correlation to adenine (r= −0.91) and amide II protein (r= 0.94) vibrational modes. This work establishes vibrational fingerprinting as a direct measure of T cell exhaustion beyond surface marker expression towards scalable immune diagnostics, in-line monitoring, and selective immunopheresis.

Nanoparticle metrology has long been constrained by the assumption that, in mixed and unprocessed fluids, particle size, morphology, composition, and species-specific abundance cannot be resolved simultaneously from a single label-free measurement. Here, we revisit this long-standing limitation by showing that complex forward speckle-holographic fields define an information-rich optical space for multidimensional particle signatures. We report deep speckle holography, a physics-informed generative framework that profiles particle identity, size, morphology, and species-resolved abundance from a single non-contact optical measurement. Across purified suspensions, mixed particle populations, environmental waters, human urine, and other unprocessed native fluids, the method enables direct nanoparticle inference without purification, labeling, or destructive preprocessing, delivering concurrent multidimensional readouts in 0.9 s over a dynamic range spanning 10 orders of magnitude. Deep speckle holography establishes a route toward direct label-free nanoparticle phenotyping in real-world fluids, moving nanoscale measurement beyond isolated-particle characterization toward multidimensional inference in complex mixtures, and expanding the scope of questions nanoscale measurement can address, from real-time tracking of nanoparticle transformations in living and environmental systems to non-invasive quality control of nanomedicine formulations, and beyond.

Nanoparticle metrology has long been constrained by the assumption that, in mixed and unprocessed fluids, particle size, morphology, composition, and species-specific abundance cannot be resolved simultaneously from a single label-free measurement. Here, we revisit this long-standing limitation by showing that complex forward speckle-holographic fields define an information-rich optical space for multidimensional particle signatures. We report deep speckle holography, a physics-informed generative framework that profiles particle identity, size, morphology, and species-resolved abundance from a single non-contact optical measurement. Across purified suspensions, mixed particle populations, environmental waters, human urine, and other unprocessed native fluids, the method enables direct nanoparticle inference without purification, labeling, or destructive preprocessing, delivering concurrent multidimensional readouts in 0.9 s over a dynamic range spanning 10 orders of magnitude. Deep speckle holography establishes a route toward direct label-free nanoparticle phenotyping in real-world fluids, moving nanoscale measurement beyond isolated-particle characterization toward multidimensional inference in complex mixtures, and expanding the scope of questions nanoscale measurement can address, from real-time tracking of nanoparticle transformations in living and environmental systems to non-invasive quality control of nanomedicine formulations, and beyond.

Abstract Adoptive cell therapies are transforming the treatment of cancer and autoimmunity by enhancing patients’ own immune cells to fight disease. In cell therapy manufacturing, immunomagnetic beads are used to isolate and activate target cells for gene transfer but must be removed downstream to ≤10 beads per 300,000 cells. Current quantification requires time-intensive and error-prone manual counting using brightfield microscopy, while existing automated approaches struggle with variable bead-cell morphology and tedious sample preparation steps. Raman spectroscopy offers rapid, morphology-independent detection using molecular signatures generated by inelastic light scattering. Here, we leverage immunomagnetic beads’ strong Raman signatures to quantify them in area scans from dried samples, achieving single bead resolution and accurate counting of bead clusters with and without cells. Using low power (≤7 mW) and exposure times (≥0.5 s), the average area under 3 signature Raman peaks (1110 cm -1 , 1346 cm -1 , and 1595 cm -1 ) are measured and input to a linear regression model, achieving a mean squared error (MSE) of <0.2 beads. Our results show Raman spectroscopy as a robust, automated approach for bead counting in existing pipelines with potential to improve the safety and throughput of cell therapies.

Volatile organic compounds (VOCs) are valuable health indicators, with synthetic breath biomarkers offering rapid and disease-specific diagnostics. However, their <100 ppb level exhalation requires mass spectrometry, limiting clinical integration. Surface-enhanced Raman spectroscopy (SERS) offers a portable, cost-effective alternative. Yet, detecting synthetic breath biomarkers, with inherently low Raman cross-sections, at <100 ppb remains challenging. We demonstrate SERS detection down to clinically relevant 10 ppb via water-mediated trapping in hydroxylated nanoporous silica-coated plasmonic nanogaps, using pentafluoropropylamine (PFP) as a representative synthetic breath biomarker. Uniform nanogaps, with >10 3 electric field enhancement, were generated between a gold film and gold–silica core–shell nanoparticle assemblies using electric field-driven evaporation. Oxygen plasma treatment hydroxylated the silica, enabling water-mediated hydrogen bonding that strengthened PFP adsorption, confirmed by density functional theory. This mechanism improved SERS sensitivity by 10 4 -fold, enabling ppb level PFP detection in mouse bronchial fluid and establishing a VOC capturing SERS platform for breath-based diagnostics.

Innovation in biomaterials has brought both breakthroughs and challenges in medicine, as implant materials have become increasingly multifunctional and complex. One of the greatest issues is the difficulty in assessing the temporal and multidimensional dynamics of tissue–implant interactions. Implant biology remains difficult to decipher without a noninvasive and multiplexed technique that can accurately monitor real-time biological processes. To address this, we developed a multifunctional, self-sensing implant material composed of gold nanocolumns patterned on a titanium surface (AuNC-Ti). This material acts as a nanoengineered surface-enhanced Raman spectroscopy (SERS) substrate that amplifies biological Raman signals at the tissue–implant interface, providing the ability to sense tissue–material interactions in a multiplexed and nondestructive manner. AuNC-Ti SERS substrates were fabricated using oblique angle deposition (OAD) and characterized using scanning electron microscopy (SEM) to show uniform formation of AuNCs (360 ± 40 nm in length and 50 ± 16 nm in width). X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and contact angle measurements demonstrated a biocompatible surface chemistry with ideal wettability. Biocompatibility was further demonstrated via in vitro cytotoxicity assays on human aortic endothelial cells (HAECs) cultured on AuNC-Ti surfaces. The median SERS enhancement factor (EF) was calculated to be 1.8 × 10 5, and spatial identification of reporter molecules and porcine tissue components on AuNC-Ti surfaces was demonstrated by using confocal Raman imaging and multivariate analysis. Our approach utilizes unlabeled SERS and machine learning techniques, promising multiplexed characterization of tissue–material interactions and subsequently enabling tissue state determination and noninvasive monitoring of implant–tissue interaction.

Sodium-ion batteries are a promising, lower-cost alternative to lithium-ion batteries, relying on earth-abundant materials less vulnerable to supply chain risks. However, further improvements in electrochemical performance are still needed to be competitive. One strategy towards this goal has been enabling reversible oxygen redox in layered NaTMO2 materials, which typically leads to degradation through structural collapse and O loss. Na2Mn3O7 has emerged as a potential solution since its ordered transition metal (TM) vacancies introduce a kinetic barrier for lattice rearrangement. We previously improved the electrochemical performance of this unique structure through Fe substitution, but the impact on TM redox mechanisms is not well understood. Further, this specific lattice undergoes a unique type of structural evolution during cycling for which a thorough understanding is currently lacking in the literature. In this study, we use advanced in-situ characterization methods to elucidate the changes in electronic and lattice structure during cycling and offer a detailed physical model of structural evolution from a P-1 towards a P2_1/c space group. These mechanistic insights will enable the design of materials that harness reversible oxygen redox and stable structures, achieving the performance needed for sodium-ion batteries to compete with lithium-ion batteries.

Antimicrobial resistance is expected to claim 10 million lives per year by 2050, and resource-limited regions are most affected. Raman spectroscopy is a novel pathogen diagnostic approach promising rapid and portable antibiotic resistance testing within a few hours, compared to days when using gold standard methods. However, current algorithms for Raman spectra analysis 1) are unable to generalize well on limited datasets across diverse patient populations and 2) require increased complexity due to the necessity of non-trivial pre-processing steps, such as feature extraction. In this work, we address these limitations using Sharpness-Aware Minimization (SAM) to enhance model generalization across a diverse array of hyperparameters in clinical bacterial isolate classification tasks. We demonstrate that SAM achieves accuracy improvements of up to 10.5% on a single split, and an increase in average accuracy of 2.7% across all splits in spectral classification tasks over the traditional optimizer, Adam. These results display the capability of SAM to advance the clinical application of AI-powered Raman spectroscopy tools. Code is available at: https://github.com/Tadesse-Lab/SAM-Raman-Diagnostics

Pediatric pneumonia requires a rapid diagnostic technique that does not rely on sputum production. Recently engineered breath biomarkers that release infection-specific volatile organic compounds (VOCs) upon inhalation present an opportunity for breath-based diagnostics, but mass spectroscopy-based detection limits use at the point-of-need. We have developed a breath-based detection scheme with surface-enhanced Raman spectroscopy and gas-capturing tubes. Conducting area scans across SERS substrates using a 50x confocal microscope, we report detection in micromolar range concentrations of representative VOCs in breath samples. With advancements in portable Raman, this design proves promising for rapid, automated diagnosis at the point-of-care.

Recent advances in generative AI and automation are transforming research and tool development across sectors. In this work we explore custom applications for faster and smoother translation of Raman spectroscopy for clinical applications. Advanced spectral analysis tools coupled with recent open access databases and web based software applications are positioned to transform the field of spectroscopy including its impact in clinical medicine, material studies and search for alternative fuels for the betterment of human and planetary health.

We present SpectroGen, an advanced, physical prior-informed generative artificial intelligence model, to facilitate transformation between various spectroscopic techniques, revolutionizing spectral acquisition approaches. Our experimental results, demonstrating over 99% correlation and less than 0.01% root mean square error, validate that our approach can effectively mitigate the limitations of spectroscopic instruments, thereby advancing material characterization and biomedical diagnostics.

Plasmonic nanostructures have wide applications in photonics including pathogen detection and diagnosis via Surface-Enhanced Raman Spectroscopy (SERS). Despite major role plasmonics play in signal enhancement, electrostatics in SERS is yet to be fully understood and harnessed. Here, we perform a systematic study of electrostatic interactions between 785 nm resonant gold nanorods designed to harbor zeta potentials of +29, +16, 0 and -9 mV spanning positive neutral and negative domains. SERS activity is tested on representative Gram-negative Escherichia coli and Gram-positive Staphylococcus epidermidis bacteria with zeta potentials of -30 and -23 mV respectively in water. Raman spectroscopy and Cryo-Electron microscopy reveal that +29, +16, 0 and -9 mV nanorods give SERS enhancement of 7.2X, 3.6X, 4.2X, 1.3X to Staphylococcus epidermidis and 3.9X, 2.8X, 2.9X, 1.1X to Escherichia coli. Theoretical results show that electrostatics play the major role among all interaction forces in determining cell-nanorod proximity and signal enhancement. We identify steric repulsion due to cell protrusions to be the critical opposing force. Finally, a design principle is proposed to estimate the electrostatic strength in SERS. Our work provides new insights into the principle of bacteria-nanorod interactions, enabling reproducible and precise biomolecular readouts, critical for next-generation point-of-care diagnostics and smart healthcare applications.

Spectroscopy is a powerful analytical technique for characterizing matter across physical and biological realms1-5. However, its fundamental principle necessitates specialized instrumentation per physical phenomena probed, limiting broad adoption and use in all relevant research. In this study, we introduce SpectroGen, a novel physical prior-informed deep generative model for generating relevant spectral signatures across modalities using experimentally collected spectral input only from a single modality. We achieve this by reimagining the representation of spectral data as mathematical constructs of distributions instead of their traditional physical and molecular state representations. The results from 319 standard mineral samples tested demonstrate generating with 99% correlation and 0.01 root mean square error with superior resolution than experimentally acquired ground truth spectra. We showed transferring capability across Raman, Infrared, and X-ray Diffraction modalities with Gaussian, Lorentzian, and Voigt distribution priors respectively6-10. This approach however is globally generalizable for any spectral input that can be represented by a distribution prior, making it universally applicable. We believe our work revolutionizes the application sphere of spectroscopy, which has traditionally been limited by access to the required sophisticated and often expensive equipment towards accelerating material, pharmaceutical, and biological discoveries.

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

(Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification of Mtb and rapid antibiotic susceptibility testing (AST) are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the MtB complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and in patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all 5 BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5000. We show how this instrument and our machine learning model enables combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Abstract Dynabeads are superparamagnetic particles used for the immunomagnetic purification of cells and biomolecules. Post‐capture, however, target identification relies on tedious culturing, fluorescence staining, and/or target amplification. Raman spectroscopy presents a rapid detection alternative, but current implementations target cells themselves with weak Raman signals. We present antibody‐coated Dynabeads as strong Raman reporter labels whose effect can be considered a Raman parallel of immunofluorescent probes. Recent developments in techniques for separating target‐bound Dynabeads from unbound Dynabeads make such an implementation feasible with high specificity. We deploy Dynabeads anti ‐Salmonella to bind and identify Salmonella enterica , a major foodborne pathogen. Dynabeads present major peaks around 1000 and 1600 cm −1 from aliphatic and aromatic C‐C stretching of the polystyrene coating and near 1350 cm −1 from the ɣ‐Fe 2 O 3 and Fe 3 O 4 core, confirmed with electron dispersive X‐ray (EDX) imaging. Minor to no contributions are made from the surface antibodies themselves as confirmed by Raman analysis of surface‐activated, antibody‐free beads. Dynabeads' Raman signature can be measured in dry and liquid samples even at single shot ~30 × 30 μm area imaging using 0.5 s, 7‐mW laser acquisition with single and clustered beads providing a 44‐ and 68‐fold larger Raman intensity compared with the signature from cells. Higher polystyrene and iron oxide content in clusters yields larger signal intensity, and conjugation to bacteria strengthens clustering as a bacterium can bind to more than one bead as observed via transmission electron microscopy (TEM). Our findings shed light on the intrinsic Raman reporter nature of Dynabeads. When combined with emerging techniques for the separation of target‐bound Dynabeads from unbound Dynabeads such as using centrifugation through a density media bilayer, they have the potential to demonstrate their dual function for target isolation and detection without tedious staining steps or unique plasmonic substrate engineering, advancing their applications in heterogeneous samples like food, water, and blood.

m area imaging using 0.5 s, 7 mW laser acquisition with single and clustered beads providing a 44- and 68-fold larger Raman intensity compared to signature from cells. Higher polystyrene and iron oxide content in clusters yields larger signal intensity and conjugation to bacteria strengthens clustering as a bacterium can bind to more than one bead as observed via transmission electron microscopy (TEM). Our findings shed light on the intrinsic Raman reporter nature of Dynabeads. When combined with emerging techniques for the separation of target-bound Dynabeads from unbound Dynabeads such as using centrifugation through a density media bi-layer, they have potential to demonstrate their dual function for target isolation and detection without tedious staining steps or unique plasmonic substrate engineering, advancing their applications in heterogeneous samples like food, water, and blood.

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths annually and 10 million new cases reported each year. The causative organism, Mycobacterium tuberculosis (Mtb) can take nearly 40 days to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification of Mtb and rapid antibiotic susceptibility testing (AST) are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the MtB complex strain Bacillus Calmette Guerin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin and amikacin, as well as a pan susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and in patient sputum samples. On dried samples, we achieve &gt;98% resistant versus susceptible classification accuracy across all 5 BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs &lt;$5000. We show how this instrument and our machine learning model enables combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Dynabeads are superparamagnetic particles used for immunomagnetic purification of cells and biomolecules. Post-capture, however, target identification relies on tedious culturing, fluorescence staining and/or target amplification. Raman spectroscopy presents a rapid detection alternative, but current implementations target cells themselves with weak Raman signals. We present antibody-coated Dynabeads as strong Raman reporter labels whose effect can be considered a Raman parallel of immunofluorescent probes. Recent developments in techniques for separating target-bound Dynabeads from unbound Dynabeads makes such an implementation feasible. We deploy Dynabeads anti-Salmonella to bind and identify Salmonella enterica, a major foodborne pathogen. Dynabeads present signature peaks at 1000 and 1600 1/cm from aliphatic and aromatic C-C stretching of polystyrene, and 1350 1/cm and 1600 1/cm from amide, alpha-helix and beta-sheet of antibody coatings of the Fe2O3 core, confirmed with electron dispersive X-ray (EDX) imaging. Their Raman signature can be measured in dry and liquid samples even at single shot ~30 x 30-micrometer area imaging using 0.5 s, 7 mW laser acquisition with single and clustered beads providing a 44- and 68-fold larger Raman intensity compared to signature from cells. Higher polystyrene and antibody content in clusters yields to the larger signal intensity and conjugation to bacteria strengthens clustering as a bacterium can bind to more than one bead as observed via transmission electron microscopy (TEM). Our findings shed light on the intrinsic Raman reporter nature of Dynabeads, demonstrating their dual function for target isolation and detection without additional sample preparation, staining, or unique plasmonic substrate engineering, advancing their applications in heterogeneous samples like food, water, and blood.

High Resolution Image Download MS PowerPoint Slide Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.