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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
Website: Research Website
Phone: 803-777-3182
Office: Swearingen
301 Main Street
Room 2C07
Columbia, SC 29208
Resources: Google Scholar
Headshot of Professor Chang Liu

 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).

See more on Google Scholar


  • BMEN 321 – Biomonitoring and Electrophysiology
  • BMEN 589 - Biosensing Fundamentals and Applications

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