College of Engineering and Computing
Faculty and Staff
Chang Liu
Title: | Associate Professor, Chemical Engineering, Biomedical Engineering |
Department: | Chemical Engineering, Biomedical Engineering College of Engineering and Computing |
Email: | changliu@cec.sc.edu |
Website: | Research Website |
Phone: | 803-777-3182 |
Office: | Swearingen 301 Main Street Room 2C07 Columbia, SC 29208 |
Resources: | Google Scholar |
Experience and Education
- Associate Professor, Biomedical Engineering, Univ. of South Carolina, 2023-Present
- Assistant Professor, Biomedical Engineering, Univ. of South Carolina, 2018-2023
- Assistant Research Scientist, The Biodesign Inst., Arizona State Univ., 2016-2018
- Postdoctoral Fellow, Houston Methodist Research Institute, 2013-2016
- Ph.D., Biomedical Engineering, Florida International University, 2013
- B.S., Biomedical Engineering, Beijing Jiaotong University, China, 2007
Research Overview
Point-Of-Care Testing and Lab Testing Assays and Devices for Protein Biomarkers of
Various Diseases
Disease biomarkers include two major categories: proteins and nucleic acids. In diagnostic
testing of many diseases (e.g. HIV, Tuberculosis, COVID-19), nucleic acids usually exhibit higher sensitivity because
they can be amplified exponentially, thus are detectable earlier after the onset of
a disease. However, in many cases, such as HIV, there is no evidence showing that
RNA appears ahead of antigen. The major challenge for antigen detection is that proteins
cannot be amplified like nucleic acids, leading to the widely held belief that antigen
(protein biomarker) tests are relatively insensitive and therefore have a limited
clinical utility, which is in fact a technological issue. Our lab has developed a
click chemistry amplified nanopore (CAN) assay for ultrasensitive circulating antigen
quantification. This assay achieved ultralow detection limit in human blood, demonstrating
higher analytical sensitivity than latest immunoassays and clinically used benchmark
ELISA. Clinical evaluation in HIV and TB patients demonstrated superiority of the
CAN assay for quantification at ultra-low concentration range in patients missed by
traditional methods. We are currently developing a streamlined automatic device including
a cost-effective microfluidic chip for sample preparation and a nanopore reader for
quantification of protein biomarkers in clinical lab testing, POCT, and self-testing
applications.
Machine Learning-Assisted Sequencing-by-Degradation for Single-Molecule Protein Sequencing
Revealing the primary sequence of a protein or peptide is essential to its identification
and function. Protein sequencing is commonly performed using mass spectrometry (MS),
a technique that involves fractionating the protein into many smaller peptides and
then obtaining the mass-to-charge ratio of each new peptide. Recently, efforts have
been made to develop single-molecule sequencing techniques for proteins similar to
what were developed for nucleic acids. Comparing to MS, these single-molecule technologies
have many desirable advantages: experimental simplicity; cost efficiency of instruments;
potential portability; and robustness. While nanopore sequencing has matured in gene
sequencing, similar methods are being explored for protein sequencing. Successful
protein sequencing requires precisely identifying and locating each AA. A crucial
point for nanopore sensing is the effective diameter and length of the sensing region
(i.e. the constriction). Our group designed a “Sequencing-by-Degradation (SBD)” method,
in which the N-terminal AAs of a peptide sample is chemically derivatized and cleaved,
and then identified by a nanopore in a cyclic manner to reconstitute its sequence.
The scientific advantage of this method lies in: higher sequencing accuracy and coverage
than existing single-molecule methods; single-AA resolution by separately reading
each AA; universal chemistry for degradation of all AAs in natural proteins; compatibility
with routine pre-treatments for complex samples; and algorithms designed for identification
in heterogeneous samples.
Toxin and Pollutant Identification, Detection, and Interaction
Per- and polyfluoroalkyl substances (PFAS) manufactured and used in various industries
are very persistent in the environment. Accumulation of PFAS in human body through
food and water can lead to adverse health outcomes. Rapid and precise detection and
identification of these environmental toxins is essential to protecting human health.
We engineered nanopores by modifying the pore lumen with cyclodextrins as adapters
to enable sensing of small PFAS molecules. Our results showed detection and identification
of various PFOA-related and PFOS-related compounds, all are members of the PFAS family
with slight size differences. Using this platform, we also successfully monitored
a detoxification process that reverses the binding of PFOA to human serum albumin in vitro.
Selected Publications
Wang, X.; Wei, X.; Van der Zalm, M.; Zhang, Z.; Subramanian, N.; Demers, A-M.; Walters, E.; Hesseling, A.; Liu, C.; Quantitation of Circulating Mycobacterium tuberculosis Antigens by Nanopore Biosensing in Children Evaluated for Pulmonary Tuberculosis in South Africa. ACS Nano, Accepted.
Wei, X.; Penkauskas, T.; Reiner, J.E.; Kennard, C.; Uline, M.J.; Wang, Q.; Li, S.; Aksimentiev, A.; Robertson, J.W.F.; Liu, C.; Engineering Biological Nanopore Approaches toward Protein Sequencing. ACS Nano, Accepted.
Wei, X.; Wang, X.; Zhang, Z.; Luo, Y.; Wang, Z.; Xiong, W.; Jain, P.K.; Monnier, J.R.; Wang, H.; Hu, T.Y.; Tang, C.; Albrecht, H.; Liu, C.; A Click Chemistry Amplified Nanopore Assay for Ultrasensitive Quantification of HIV-1 p24 Antigen in Clinical Samples. Nature Communications, 13, 6852 (2022).
Zhang, Z.; Wang, X.; Wei, X.; Zheng, S.W.; Lenhart, B.J.; Xu, P.; Li, J.; Pan, J.; Albrecht, H.; Liu, C.; Multiplex Quantitative Detection of SARS-CoV-2 Specific IgG and IgM Antibodies based on DNA-Assisted Nanopore Sensing. Biosensors and Bioelectronics, 181, 113134 (2021).
Wei, X.; Ma, D.; Zhang, Z.; Wang, L.Y.; Gray, J.L.; Zhang, L.; Zhu, T.; Wang, X.; Lenhart, B.J.; Yin, Y.; Wang, Q.; Liu, C.; N-terminal Derivatization-Assisted Identification of Individual Amino Acids using a Biological Nanopore Sensor. ACS sensors, 5, 1707 (2020).
Teaching
- BMEN 321 – Biomonitoring and Electrophysiology
- BMEN 589 - Biosensing Fundamentals and Applications