I have long term interest in combining advanced science and technology to provide next generation healthcare system.
To reach that goal, I have developed machine learning based diagnosis model on the software end, which is combined with my hardware end work including wearable/flexible electronics and microelectronic/microfludic platforms.
Honors & Awards
Baxter Young Investigator Award, Baxter International (2022.09)
Sanjiv Sam Gambhir-Phillips Fellowship, Stanford Precision Health and Integrated Diagnostics Center (2022.04)
Nokia Bell Labs Prize 2021 for Innovators, finalist (6/108 from worldwide), Nokia Bell Labs (2021.11)
Penn Health Tech Pioneer Award seed grant, University of Pennsylvania (2021.09)
Nemirovsky Engineering and Medicine Opportunity (NEMO) Prize, finalist, University of Pennsylvania (2021.08)
Penn Bioengineering Graduate Group Research Symposium, second place, University of Pennsylvania, Bioengineering Department (2021.01)
Doctor of Philosophy, University of Pennsylvania (2021)
PhD, University of Pennsylvania, Mechanical Engineering and Applied Mechanics
MS, University of Illinois at Urbana Champaign, Materials Science and Engineering
BS, Zhejiang University, Materials Science and Engineering
H. Tom Soh, Postdoctoral Faculty Sponsor
Ultrasensitive Single Extracellular Vesicle Detection Using High Throughput Droplet Digital Enzyme-Linked Immunosorbent Assay.
2022; 22 (11): 4315-4324
Extracellular vesicles (EVs) have attracted enormous attention for their diagnostic and therapeutic potential. However, it has proven challenging to achieve the sensitivity to detect individual nanoscale EVs, the specificity to distinguish EV subpopulations, and a sufficient throughput to study EVs among an enormous background. To address this fundamental challenge, we developed a droplet-based optofluidic platform to quantify specific individual EV subpopulations at high throughput. The key innovation of our platform is parallelization of droplet generation, processing, and analysis to achieve a throughput (∼20 million droplets/min) more than 100× greater than typical microfluidics. We demonstrate that the improvement in throughput enables EV quantification at a limit of detection = 9EVs/μL, a >100× improvement over gold standard methods. Additionally, we demonstrate the clinical potential of this system by detecting human EVs in complex media. Building on this work, we expect this technology will allow accurate quantification of rare EV subpopulations for broad biomedical applications.
View details for DOI 10.1021/acs.nanolett.2c00274
View details for PubMedID 35588529
Advancing microfluidic diagnostic chips into clinical use: a review of current challenges and opportunities.
Lab on a chip
Microfluidic diagnostic (μDX) technologies miniaturize sensors and actuators to the length-scales that are relevant to biology: the micrometer scale to interact with cells and the nanometer scale to interrogate biology's molecular machinery. This miniaturization allows measurements of biomarkers of disease (cells, nanoscale vesicles, molecules) in clinical samples that are not detectable using conventional technologies. There has been steady progress in the field over the last three decades, and a recent burst of activity catalyzed by the COVID-19 pandemic. In this time, an impressive and ever-growing set of technologies have been successfully validated in their ability to measure biomarkers in clinical samples, such as blood and urine, with sensitivity and specificity not possible using conventional tests. Despite our field's many accomplishments to date, very few of these technologies have been successfully commercialized and brought to clinical use where they can fulfill their promise to improve medical care. In this paper, we identify three major technological trends in our field that we believe will allow the next generation of μDx to have a major impact on the practice of medicine, and which present major opportunities for those entering the field from outside disciplines: 1. the combination of next generation, highly multiplexed μDx technologies with machine learning to allow complex patterns of multiple biomarkers to be decoded to inform clinical decision points, for which conventional biomarkers do not necessarily exist. 2. The use of micro/nano devices to overcome the limits of binding affinity in complex backgrounds in both the detection of sparse soluble proteins and nucleic acids in blood and rare circulating extracellular vesicles. 3. A suite of recent technologies that obviate the manual pre-processing and post-processing of samples before they are measured on a μDX chip. Additionally, we discuss economic and regulatory challenges that have stymied μDx translation to the clinic, and highlight strategies for successfully navigating this challenging space.
View details for DOI 10.1039/d2lc00024e
View details for PubMedID 35674283
A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosis and Staging of Pancreatic Ductal Adenocarcinoma
CLINICAL CANCER RESEARCH
2020; 26 (13): 3248-3258
To determine whether a multianalyte liquid biopsy can improve the detection and staging of pancreatic ductal adenocarcinoma (PDAC).We analyzed plasma from 204 subjects (71 healthy, 44 non-PDAC pancreatic disease, and 89 PDAC) for the following biomarkers: tumor-associated extracellular vesicle miRNA and mRNA isolated on a nanomagnetic platform that we developed and measured by next-generation sequencing or qPCR, circulating cell-free DNA (ccfDNA) concentration measured by qPCR, ccfDNA KRAS G12D/V/R mutations detected by droplet digital PCR, and CA19-9 measured by electrochemiluminescence immunoassay. We applied machine learning to training sets and subsequently evaluated model performance in independent, user-blinded test sets.To identify patients with PDAC versus those without, we generated a classification model using a training set of 47 subjects (20 PDAC and 27 noncancer). When applied to a blinded test set (N = 136), the model achieved an AUC of 0.95 and accuracy of 92%, superior to the best individual biomarker, CA19-9 (89%). We next used a cohort of 20 patients with PDAC to train our model for disease staging and applied it to a blinded test set of 25 patients clinically staged by imaging as metastasis-free, including 9 subsequently determined to have had occult metastasis. Our workflow achieved significantly higher accuracy for disease staging (84%) than imaging alone (accuracy = 64%; P < 0.05).Algorithmically combining blood-based biomarkers may improve PDAC diagnostic accuracy and preoperative identification of nonmetastatic patients best suited for surgery, although larger validation studies are necessary.
View details for DOI 10.1158/1078-0432.CCR-19-3313
View details for Web of Science ID 000546016400021
View details for PubMedID 32299821
View details for PubMedCentralID PMC7334066
- Biodegradable Monocrystalline Silicon Photovoltaic Microcells as Power Supplies for Transient Biomedical Implants ADVANCED ENERGY MATERIALS 2018; 8 (16)
Capacitively coupled arrays of multiplexed flexible silicon transistors for long-term cardiac electrophysiology
NATURE BIOMEDICAL ENGINEERING
2017; 1 (3)
Advanced capabilities in electrical recording are essential for the treatment of heart-rhythm diseases. The most advanced technologies use flexible integrated electronics; however, the penetration of biological fluids into the underlying electronics and any ensuing electrochemical reactions pose significant safety risks. Here, we show that an ultrathin, leakage-free, biocompatible dielectric layer can completely seal an underlying layer of flexible electronics while allowing for electrophysiological measurements through capacitive coupling between tissue and the electronics, and thus without the need for direct metal contact. The resulting current-leakage levels and operational lifetimes are, respectively, four orders of magnitude smaller and between two and three orders of magnitude longer than those of any other flexible-electronics technology. Systematic electrophysiological studies with normal, paced and arrhythmic conditions in Langendorff hearts highlight the capabilities of the capacitive-coupling approach. Our technology provides a realistic pathway towards the broad applicability of biocompatible, flexible electronic implants.
View details for DOI 10.1038/s41551-017-0038
View details for Web of Science ID 000418854800003
View details for PubMedID 28804678
View details for PubMedCentralID PMC5552067
Proteomic Profiling of Extracellular Vesicles Separated from Plasma of Former National Football League Players at Risk for Chronic Traumatic Encephalopathy
AGING AND DISEASE
2021; 12 (6): 1363-1375
Chronic Traumatic Encephalopathy (CTE) is a tauopathy that affects individuals with a history of exposure to repetitive head impacts, including National Football League (NFL) players. Extracellular vesicles (EVs) are known to carry tau in Alzheimer's disease and other tauopathies. We examined protein profiles of EVs separated from the plasma of former NFL players at risk for CTE. EVs were separated from the plasma from former NFL players and age-matched controls using size-exclusion chromatography. Label-free quantitative proteomic analysis identified 675 proteins in plasma EVs, and 17 proteins were significantly differentially expressed between former NFL players and controls. Total tau (t-tau) and tau phosphorylated at threonie181 (p-tau181) in plasma-derived EVs were measured by ultrasensitive immunoassay. Level of t-tau and p-tau181 in EVs were significantly different, and the area under the receiver operating characteristic curve (AUC) of t-tau and p-tau181 showed 0.736 and 0.715, respectively. Machine learning analysis indicated that a combination of collagen type VI alpha 3 and 1 chain (COL6A3 and COL6A1) and reelin (RELN) can distinguish former NFL players from controls with 85% accuracy (AUC = 0.85). Based on the plasma EV proteomics, these data provide protein profiling of plasma EVs for CTE, and indicate combination of COL6A3, RELN and COL6A1 in plasma EVs may serve as the potential diagnostic biomarkers for CTE.
View details for DOI 10.14336/AD.2020.0908
View details for Web of Science ID 000696313300002
View details for PubMedID 34527415
View details for PubMedCentralID PMC8407879
Extracellular vesicles as distinct biomarker reservoirs for mild traumatic brain injury diagnosis
2021; 3 (3): fcab151
Mild traumatic brain injury does not currently have a clear molecular diagnostic panel to either confirm the injury or to guide its treatment. Current biomarkers for traumatic brain injury rely mainly on detecting circulating proteins in blood that are associated with degenerating neurons, which are less common in mild traumatic brain injury, or with broad inflammatory cascades which are produced in multiple tissues and are thus not brain specific. To address this issue, we conducted an observational cohort study designed to measure a protein panel in two compartments-plasma and brain-derived extracellular vesicles-with the following hypotheses: (i) each compartment provides independent diagnostic information and (ii) algorithmically combining these compartments accurately classifies clinical mild traumatic brain injury. We evaluated this hypothesis using plasma samples from mild (Glasgow coma scale scores 13-15) traumatic brain injury patients (n = 47) and healthy and orthopaedic control subjects (n = 46) to evaluate biomarkers in brain-derived extracellular vesicles and plasma. We used our Track Etched Magnetic Nanopore technology to isolate brain-derived extracellular vesicles from plasma based on their expression of GluR2, combined with the ultrasensitive digital enzyme-linked immunosorbent assay technique, Single-Molecule Array. We quantified extracellular vesicle-packaged and plasma levels of biomarkers associated with two categories of traumatic brain injury pathology: neurodegeneration and neuronal/glial damage (ubiquitin C-terminal hydrolase L1, glial fibrillary acid protein, neurofilament light and Tau) and inflammation (interleukin-6, interleukin-10 and tumour necrosis factor alpha). We found that GluR2+ extracellular vesicles have distinct biomarker distributions than those present in the plasma. As a proof of concept, we showed that using a panel of biomarkers comprised of both plasma and GluR2+ extracellular vesicles, injured patients could be accurately classified versus non-injured patients.
View details for DOI 10.1093/braincomms/fcab151
View details for Web of Science ID 000734327400024
View details for PubMedID 34622206
View details for PubMedCentralID PMC8491985
Micro- and Nano-Devices for Studying Subcellular Biology
2021; 17 (3): e2005793
Cells are complex machines whose behaviors arise from their internal collection of dynamically interacting organelles, supramolecular complexes, and cytoplasmic chemicals. The current understanding of the nature by which subcellular biology produces cell-level behaviors is limited by the technological hurdle of measuring the large number (>103 ) of small-sized (<1 μm) heterogeneous organelles and subcellular structures found within each cell. In this review, the emergence of a suite of micro- and nano-technologies for studying intracellular biology on the scale of organelles is described. Devices that use microfluidic and microelectronic components for 1) extracting and isolating subcellular structures from cells and lysate; 2) analyzing the physiology of individual organelles; and 3) recreating subcellular assembly and functions in vitro, are described. The authors envision that the continued development of single organelle technologies and analyses will serve as a foundation for organelle systems biology and will allow new insight into fundamental and clinically relevant biological questions.
View details for DOI 10.1002/smll.202005793
View details for Web of Science ID 000600361200001
View details for PubMedID 33345457
View details for PubMedCentralID PMC8258219
Proteomic and biological profiling of extracellular vesicles from Alzheimer's disease human brain tissues
ALZHEIMERS & DEMENTIA
2020; 16 (6): 896-907
Extracellular vesicles (EVs) from human Alzheimer's disease (AD) biospecimens contain amyloid beta (Aβ) peptide and tau. While AD EVs are known to affect brain disease pathobiology, their biochemical and molecular characterizations remain ill defined.EVs were isolated from the cortical gray matter of 20 AD and 18 control brains. Tau and Aβ levels were measured by immunoassay. Differentially expressed EV proteins were assessed by quantitative proteomics and machine learning.Levels of pS396 tau and Aβ1-42 were significantly elevated in AD EVs. High levels of neuron- and glia-specific factors are detected in control and AD EVs, respectively. Machine learning identified ANXA5, VGF, GPM6A, and ACTZ in AD EV compared to controls. They distinguished AD EVs from controls in the test sets with 88% accuracy.In addition to Aβ and tau, ANXA5, VGF, GPM6A, and ACTZ are new signature proteins in AD EVs.
View details for DOI 10.1002/alz.12089
View details for Web of Science ID 000577870000008
View details for PubMedID 32301581
View details for PubMedCentralID PMC7293582
Multi-Dimensional Mapping of Brain-Derived Extracellular Vesicle MicroRNA Biomarker for Traumatic Brain Injury Diagnostics
JOURNAL OF NEUROTRAUMA
2020; 37 (22): 2424-2434
The diagnosis and prognosis of traumatic brain injury (TBI) is complicated by variability in the type and severity of injuries and the multiple endophenotypes that describe each patient's response and recovery to the injury. It has been challenging to capture the multiple dimensions that describe an injury and its recovery to provide clinically useful information. To address this challenge, we have performed an open-ended search for panels of microRNA (miRNA) biomarkers, packaged inside of brain-derived extracellular vesicles (EVs), that can be combined algorithmically to accurately classify various states of injury. We mapped GluR2+ EV miRNA across a variety of injury types, injury intensities, history of injuries, and time elapsed after injury, and sham controls in a pre-clinical murine model (n = 116), as well as in clinical samples (n = 36). We combined next-generation sequencing with a technology recently developed by our lab, Track Etched Magnetic Nanopore (TENPO) sorting, to enrich for GluR2+ EVs and profile their miRNA. By mapping and comparing brain-derived EV miRNA between various injuries, we have identified signaling pathways in the packaged miRNA that connect these biomarkers to underlying mechanisms of TBI. Many of these pathways are shared between the pre-clinical model and the clinical samples, and present distinct signatures across different injury models and times elapsed after injury. Using this map of EV miRNA, we applied machine learning to define a panel of biomarkers to successfully classify specific states of injury, paving the way for a prognostic blood test for TBI. We generated a panel of eight miRNAs (miR-150-5p, miR-669c-5p, miR-488-3p, miR-22-5p, miR-9-5p, miR-6236, miR-219a.2-3p, miR-351-3p) for injured mice versus sham mice and four miRNAs (miR-203b-5p, miR-203a-3p, miR-206, miR-185-5p) for TBI patients versus healthy controls.
View details for DOI 10.1089/neu.2018.6220
View details for Web of Science ID 000468297500001
View details for PubMedID 30950328
View details for PubMedCentralID PMC7698852
Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles
LAB ON A CHIP
2018; 18 (23): 3617-3630
The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries.
View details for DOI 10.1039/c8lc00672e
View details for Web of Science ID 000450737200005
View details for PubMedID 30357245
View details for PubMedCentralID PMC6334845
Cytotoxicity and in Vitro Degradation Kinetics of Foundry-Compatible Semiconductor Nanomembranes and Electronic Microcomponents
2018; 12 (10): 9721-9732
Foundry-compatible materials and processing approaches serve as the foundations for advanced, active implantable microsystems that can dissolve in biofluids into biocompatible reaction products, with broad potential applications in biomedicine. The results reported here include in vitro studies of the dissolution kinetics and nanoscale bioresorption behaviors of device-grade thin films of Si, SiN x, SiO2, and W in the presence of dynamic cell cultures via atomic force microscopy and X-ray photoemission spectroscopy. In situ investigations of cell-extracellular mechanotransduction induced by cellular traction provide insights into the cytotoxicity of these same materials and of microcomponents formed with them using foundry-compatible processes, indicating potential cytotoxicity elicited by W at concentrations greater than 6 mM. The findings are of central relevance to the biocompatibility of modern Si-based electronics technologies as active, bioresorbable microsystems that interface with living tissues.
View details for DOI 10.1021/acsnano.8b04513
View details for Web of Science ID 000448751800011
View details for PubMedID 30160102
Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2016; 113 (42): 11682-11687
Materials that can serve as long-lived barriers to biofluids are essential to the development of any type of chronic electronic implant. Devices such as cardiac pacemakers and cochlear implants use bulk metal or ceramic packages as hermetic enclosures for the electronics. Emerging classes of flexible, biointegrated electronic systems demand similar levels of isolation from biofluids but with thin, compliant films that can simultaneously serve as biointerfaces for sensing and/or actuation while in contact with the soft, curved, and moving surfaces of target organs. This paper introduces a solution to this materials challenge that combines (i) ultrathin, pristine layers of silicon dioxide (SiO2) thermally grown on device-grade silicon wafers, and (ii) processing schemes that allow integration of these materials onto flexible electronic platforms. Accelerated lifetime tests suggest robust barrier characteristics on timescales that approach 70 y, in layers that are sufficiently thin (less than 1 μm) to avoid significant compromises in mechanical flexibility or in electrical interface fidelity. Detailed studies of temperature- and thickness-dependent electrical and physical properties reveal the key characteristics. Molecular simulations highlight essential aspects of the chemistry that governs interactions between the SiO2 and surrounding water. Examples of use with passive and active components in high-performance flexible electronic devices suggest broad utility in advanced chronic implants.
View details for DOI 10.1073/pnas.1605269113
View details for Web of Science ID 000385610400041
View details for PubMedID 27791052
View details for PubMedCentralID PMC5081656
Materials and Fractal Designs for 3D Multifunctional Integumentary Membranes with Capabilities in Cardiac Electrotherapy
2015; 27 (10): 1731-?
Advanced materials and fractal design concepts form the basis of a 3D conformal electronic platform with unique capabilities in cardiac electrotherapies. Fractal geometries, advanced electrode materials, and thin, elastomeric membranes yield a class of device capable of integration with the entire 3D surface of the heart, with unique operational capabilities in low power defibrillation. Co-integrated collections of sensors allow simultaneous monitoring of physiological responses. Animal experiments on Langendorff-perfused rabbit hearts demonstrate the key features of these systems.
View details for DOI 10.1002/adma.201405017
View details for Web of Science ID 000350754100013
View details for PubMedID 25641076
View details for PubMedCentralID PMC4527319