Jeon Woong Kang

A vector-free microfluidic platform for intracellular delivery.

Intracellular delivery of macromolecules is a challenge in research and therapeutic applications. Existing vector-based and physical methods have limitations, including their reliance on exogenous materials or electrical fields, which can lead to toxicity or off-target effects. We describe a microfluidic approach to delivery in which cells are mechanically deformed as they pass through a constriction 30–80% smaller than the cell diameter. The resulting controlled application of compression and shear forces results in the formation of transient holes that enable the diffusion of material from the surrounding buffer into the cytosol. The method has demonstrated the ability to deliver a range of material, such as carbon nanotubes, proteins, and siRNA, to 11 cell types, including embryonic stem cells and immune cells. When used for the delivery of transcription factors, the microfluidic devices produced a 10-fold improvement in colony formation relative to electroporation and cell-penetrating peptides. Indeed, its ability to deliver structurally diverse materials and its applicability to difficult-to-transfect primary cells indicate that this method could potentially enable many research and clinical applications.

High resolution live cell Raman imaging using subcellular organelle-targeting SERS-sensitive gold nanoparticles with highly narrow intra-nanogap.

We report a method to achieve high speed and high resolution live cell Raman images using small spherical gold nanoparticles with highly narrow intra-nanogap structures responding to NIR excitation (785 nm) and high-speed confocal Raman microscopy. The three different Raman-active molecules placed in the narrow intra-nanogap showed a strong and uniform Raman intensity in solution even under transient exposure time (10 ms) and low input power of incident laser (200 μW), which lead to obtain high-resolution single cell image within 30 s without inducing significant cell damage. The high resolution Raman image showed the distributions of gold nanoparticles for their targeted sites such as cytoplasm, mitochondria, or nucleus. The high speed Raman-based live cell imaging allowed us to monitor rapidly changing cell morphologies during cell death induced by the addition of highly toxic KCN solution to cells. These results strongly suggest that the use of SERS-active nanoparticle can greatly improve the current temporal resolution and image quality of Raman-based cell images enough to obtain the detailed cell dynamics and/or the responses of cells to potential drug molecules.

Combinatorial hydrogel library enables identification of materials that mitigate the foreign body response in primates

The foreign body response is an immune-mediated reaction that can lead to the failure of implanted medical devices and discomfort for the recipient. There is a critical need for biomaterials that overcome this key challenge in the development of medical devices. Here we use a combinatorial approach for covalent chemical modification to generate a large library of variants of one of the most widely used hydrogel biomaterials, alginate. We evaluated the materials in vivo and identified three triazole-containing analogs that substantially reduce foreign body reactions in both rodents and, for at least 6 months, in non-human primates. The distribution of the triazole modification creates a unique hydrogel surface that inhibits recognition by macrophages and fibrous deposition. In addition to the utility of the compounds reported here, our approach may enable the discovery of other materials that mitigate the foreign body response.

Raman Spectroscopy Provides a Powerful Diagnostic Tool for Accurate Determination of Albumin Glycation

We present the first demonstration of glycated albumin detection and quantification using Raman spectroscopy without the addition of reagents. Glycated albumin is an important marker for monitoring the long-term glycemic history of diabetics, especially as its concentrations, in contrast to glycated hemoglobin levels, are unaffected by changes in erythrocyte life times. Clinically, glycated albumin concentrations show a strong correlation with the development of serious diabetes complications including nephropathy and retinopathy. In this article, we propose and evaluate the efficacy of Raman spectroscopy for determination of this important analyte. By utilizing the pre-concentration obtained through drop-coating deposition, we show that glycation of albumin leads to subtle, but consistent, changes in vibrational features, which with the help of multivariate classification techniques can be used to discriminate glycated albumin from the unglycated variant with 100% accuracy. Moreover, we demonstrate that the calibration model developed on the glycated albumin spectral dataset shows high predictive power, even at substantially lower concentrations than those typically encountered in clinical practice. In fact, the limit of detection for glycated albumin measurements is calculated to be approximately four times lower than its minimum physiological concentration. Importantly, in relation to the existing detection methods for glycated albumin, the proposed method is also completely reagent-free, requires barely any sample preparation and has the potential for simultaneous determination of glycated hemoglobin levels as well. Given these key advantages, we believe that the proposed approach can provide a uniquely powerful tool for quantification of glycation status of proteins in biopharmaceutical development as well as for glycemic marker determination in routine clinical diagnostics in the future.

Raman spectroscopy-based sensitive and specific detection of glycated hemoglobin.

In recent years, glycated hemoglobin (HbA1c) has been increasingly accepted as a functional metric of mean blood glucose in the treatment of diabetic patients. Importantly, HbA1c provides an alternate measure of total glycemic exposure due to the representation of blood glucose throughout the day, including post-prandially. In this article, we propose and demonstrate the potential of Raman spectroscopy as a novel analytical method for quantitative detection of HbA1c, without using external dyes or reagents. Using the drop coating deposition Raman (DCDR) technique, we observe that the nonenzymatic glycosylation (glycation) of the hemoglobin molecule results in subtle but discernible and highly reproducible changes in the acquired spectra, which enable the accurate determination of glycated and nonglycated hemoglobin using standard chemometric methods. The acquired Raman spectra display excellent reproducibility of spectral characteristics at different locations in the drop and show a linear dependence of the spectral intensity on the analyte concentration. Furthermore, in hemolysate models, the developed multivariate calibration models for HbA1c show a high degree of prediction accuracy and precision--with a limit of detection that is a factor of ~15 smaller than the lowest physiological concentrations encountered in clinical practice. The excellent accuracy and reproducibility achieved in this proof-of-concept study opens substantive avenues for characterization and quantification of the glycosylation status of (therapeutic) proteins, which are widely used for biopharmaceutical development. We also envision that the proposed approach can provide a powerful tool for high-throughput HbA1c sensing in multicomponent mixtures and potentially in hemolysate and whole blood lysate samples.

Combined confocal Raman and quantitative phase microscopy system for biomedical diagnosis

We have developed a novel multimodal microscopy system that incorporates confocal Raman, confocal reflectance, and quantitative phase microscopy (QPM) into a single imaging entity. Confocal Raman microscopy provides detailed chemical information from the sample, while confocal reflectance and quantitative phase microscopy show detailed morphology. Combining these intrinsic contrast imaging modalities makes it possible to obtain quantitative morphological and chemical information without exogenous staining. For validation and characterization, we have used this multi-modal system to investigate healthy and diseased blood samples. We first show that the thickness of a healthy red blood cell (RBC) shows good correlation with its hemoglobin distribution. Further, in malaria infected RBCs, we successfully image the distribution of hemozoin (malaria pigment) inside the cell. Our observations lead us to propose morphological screening by QPM and subsequent chemical imaging by Raman for investigating blood disorders. This new approach allows monitoring cell development and cell-drug interactions with minimal perturbation of the biological system of interest.

Diffraction optical tomography using a quantitative phase imaging unit

A simple and practical method to measure three-dimensional (3-D) refractive index (RI) distributions of biological cells is presented. A common-path self-reference interferometry consisting of a compact set of polarizers is attached to a conventional inverted microscope equipped with a beam scanning unit, which can precisely measure multiple 2-D holograms of a sample with high phase stability for various illumination angles, from which accurate 3-D optical diffraction tomograms of the sample can be reconstructed. 3-D RI tomograms of nonbiological samples such as polystyrene microspheres, as well as biological samples including human red blood cells and breast cancer cells, are presented.

Portable Optical Fiber Probe-Based Spectroscopic Scanner for Rapid Cancer Diagnosis: A New Tool for Intraoperative Margin Assessment

There continues to be a significant clinical need for rapid and reliable intraoperative margin assessment during cancer surgery. Here we describe a portable, quantitative, optical fiber probe-based, spectroscopic tissue scanner designed for intraoperative diagnostic imaging of surgical margins, which we tested in a proof of concept study in human tissue for breast cancer diagnosis. The tissue scanner combines both diffuse reflectance spectroscopy (DRS) and intrinsic fluorescence spectroscopy (IFS), and has hyperspectral imaging capability, acquiring full DRS and IFS spectra for each scanned image pixel. Modeling of the DRS and IFS spectra yields quantitative parameters that reflect the metabolic, biochemical and morphological state of tissue, which are translated into disease diagnosis. The tissue scanner has high spatial resolution (0.25 mm) over a wide field of view (10 cm×10 cm), and both high spectral resolution (2 nm) and high spectral contrast, readily distinguishing tissues with widely varying optical properties (bone, skeletal muscle, fat and connective tissue). Tissue-simulating phantom experiments confirm that the tissue scanner can quantitatively measure spectral parameters, such as hemoglobin concentration, in a physiologically relevant range with a high degree of accuracy (<5% error). Finally, studies using human breast tissues showed that the tissue scanner can detect small foci of breast cancer in a background of normal breast tissue. This tissue scanner is simpler in design, images a larger field of view at higher resolution and provides a more physically meaningful tissue diagnosis than other spectroscopic imaging systems currently reported in literatures. We believe this spectroscopic tissue scanner can provide real-time, comprehensive diagnostic imaging of surgical margins in excised tissues, overcoming the sampling limitation in current histopathology margin assessment. As such it is a significant step in the development of a platform technology for intraoperative management of cancer, a clinical problem that has been inadequately addressed to date.

Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements

Although several in vivo blood glucose measurement studies have been performed by different research groups using near-infrared (NIR) absorption and Raman spectroscopic techniques, prospective prediction has proven to be a challenging problem. An important issue in this case is the demonstration of causality of glucose concentration to the spectral information, especially as the intrinsic glucose signal is smaller compared with that of the other analytes in the blood–tissue matrix. Furthermore, time-dependent physiological processes make the relation between glucose concentration and spectral data more complex. In this article, chance correlations in Raman spectroscopy-based calibration model for glucose measurements are investigated for both in vitro (physical tissue models) and in vivo (animal model and human subject) cases. Different spurious glucose concentration profiles are assigned to the Raman spectra acquired from physical tissue models, where the glucose concentration is intentionally held constant. Analogous concentration profiles, in addition to the true concentration profile, are also assigned to the datasets acquired from an animal model during a glucose clamping study as well as a human subject during an oral glucose tolerance test. We demonstrate that the spurious concentration profile-based calibration models are unable to provide prospective predictions, in contrast to those based on actual concentration profiles, especially for the physical tissue models. We also show that chance correlations incorporated by the calibration models are significantly less in Raman as compared to NIR absorption spectroscopy, even for the in vivo studies. Finally, our results suggest that the incorporation of chance correlations for in vivo cases can be largely attributed to the uncontrolled physiological sources of variations. Such uncontrolled physiological variations could either be intrinsic to the subject or stem from changes in the measurement conditions.

Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy

While Raman spectroscopy provides a powerful tool for noninvasive and real time diagnostics of biological samples, its translation to the clinical setting has been impeded by the lack of robustness of spectroscopic calibration models and the size and cumbersome nature of conventional laboratory Raman systems. Linear multivariate calibration models employing full spectrum analysis are often misled by spurious correlations, such as system drift and covariations among constituents. In addition, such calibration schemes are prone to overfitting, especially in the presence of external interferences that may create nonlinearities in the spectra-concentration relationship. To address both of these issues we incorporate residue error plot-based wavelength selection and nonlinear support vector regression (SVR). Wavelength selection is used to eliminate uninformative regions of the spectrum, while SVR is used to model the curved effects such as those created by tissue turbidity and temperature fluctuations. Using glucose detection in tissue phantoms as a representative example, we show that even a substantial reduction in the number of wavelengths analyzed using SVR lead to calibration models of equivalent prediction accuracy as linear full spectrum analysis. Further, with clinical datasets obtained from human subject studies, we also demonstrate the prospective applicability of the selected wavelength subsets without sacrificing prediction accuracy, which has extensive implications for calibration maintenance and transfer. Additionally, such wavelength selection could substantially reduce the collection time of serial Raman acquisition systems. Given the reduced footprint of serial Raman systems in relation to conventional dispersive Raman spectrometers, we anticipate that the incorporation of wavelength selection in such hardware designs will enhance the possibility of miniaturized clinical systems for disease diagnosis in the near future.