Title: SBL Logo - Description: SBL Logo

Research

The Systematic Bioengineering Laboratory (SBL) at Penn State focuses on novel biosensing techniques for biomedical applications. In terms of technological development, we develop single cell biosensors and microfluidic devices based on biomimetic and nanoengineered materials and advanced manufacturing strategies. We also engage in computational methods, such as data science and artificial intelligence algorithms, in our biomedical engineering workflow. In terms of biomedical applications, we apply our technologies to elucidate the regulatory mechanisms of collective cancer invasion, develop rapid diagnostic systems for infectious diseases, and characterize the microbiota in healthy and disease conditions. 

 

Technological Development

Biosensor Design for Dynamic Single Cell Analysis. SBL has made significant contributions to live single cell biosensing for dynamic multigene analysis. For instance, we have established a GNR-LNA biosensor for mapping dynamic gene expression profiles in photothermally stimulated lung tissues, mechanically damaged mouse cornea, Nrf2 mediated chemoresistance in KRASG12D mouse lung tumor, and patient-derived tumor organoids (ACS Nano 2014 Link; Advanced Materials 2015 Link; Analytical Chemistry 2020 Link). In addition, we have demonstrated multiplex detection of both mRNA and protein in the same cell by incorporating molecular aptamers into the biosensor design (Biomaterials 2018 Link). We have also established novel biosensors for rapid identification of bacterial species at the single cell level (Nanomedicine: NBM 2019 Link).

 

http://www.bioe.psu.edu/labs/sbl/pub_files/image002.jpg

GNR-LNA biosensors

Microfluidics for 3D tissue modeling and disease diagnostics. SBL has pioneered microfluidic devices and single cell analysis techniques for medical diagnostics and tissue modeling (Small, 2019.  Link). For instance, we confine pathogens from raw or enriched patient samples in microchannels to determine the bacterial antibiotic resistance profiles at the single cell level (PNAS 2019 Link). Their growth rates and antibiotic resistance profiles can be determined at the single cell level in as few as 30 min. We have also developed bioinspired microfluidic systems for metabolic evaluation of urinary stone disease at the point of care (Science Advances 2020 Link). Our technologies are being adopted in various clinical studies and product development activities worldwide in collaboration with clinical and industrial collaborators (Nature Sustainability 2019 Link).

 

Title: Analytical Chemistry - Description: Cover image

Single cell AST device

 

 

 

Data science and artificial intelligence-based analysis. SBL is also engaged in data-driven and computational techniques (PLoS Computational Biology 2016 Link). For instance, we establish an artificial intelligence (AI)-guided experimental strategy for screening potent antiviral drug combinations and immunomodulation cocktails (PNAS 2008 Link). The AI-guided method reduces the one million possible cases in the search space into as few as ten iterations, which dramatically reduces the time and cost of the optimization process. Similarly, we have demonstrated a metamodel antimicrobial cocktail optimization (MACO) scheme to identify synergistic antibiotic cocktails that reduce the minimum inhibitory concentration 40-fold (PLoS ONE 2010 Link). We are actively working on machine learning and bioinformatic techniques for modeling complex biomedical processes and medical diagnostics. 

 

3D image analysis

 

Biomedical Applications

Infectious diseases and antimicrobial resistance.  Rapid detection of pathogenic agents is critical towards judicious management of infectious diseases, such as urinary tract infection and sepsis, especially in emergency situations and high-risk areas such as hospitals, airports, rural clinics, and temporary clinics established in response to disasters (Nature Biomedical Engineering 2020 Link).  In settings where highly infectious pathogens are suspected, point-of-care detection will lead to timely initiation of appropriate treatments, which will reduce the infected individuals’ morbidity and mortality, as well as address public health concerns by efficient triaging of the uninfected from the infected. Within this context, we design and implement microfluidic, rapid diagnostic systems to address the unmet critical need for rapid pathogen identification and antimicrobial susceptibility testing (PNAS 2019 Link). 

 

electron micrograph of bacteria

Single cell pathogen identification

 

Collective cancer invasion and leader cell formation. Collective cancer invasion is increasingly recognized as a predominant mechanism in the metastatic cascade. At the onset of collective cell migration, a subset of cells within an initially homogenous population acquires a distinct “leader” phenotype (Nature Reviews Cancer, 2020 Link). However, the molecular mechanisms driving the formation of invasive leader cells as well as the signaling network regulating their density during collective cancer invasion remain to be determined.  Using dynamic single cell gene expression analysis and computational modeling, we have shown that the leader cell identity is dynamically regulated by Dll4-Notch1 signaling and intercellular tension (Nature Communications 2015 Link). Furthermore, we have identified the pivotal role of Nrf2 in regulating the hybrid epithelial/mesenchymal phenotypes and collective invasion of cancer cells (Integrative Biology 2014 Link; Integrative Biology 2019 Link).

 

Bladder tumor organoids

Dysbiosis and microbiota. Microbiota contribute fundamentally to human health, and imbalance of microbiota (dysbiosis) is associated with various medical conditions. Patients in the hospital (e.g., ICU) are certain to experience disturbances of the microbiota either due to underlying diagnosis at admission and/or unintended consequences of medical treatment. Furthermore, the microbiota is increasingly recognized as a critical component in cancer and cancer therapy (e.g., immunotherapy). Acknowledgment and understanding of the microbiota in various medical conditions could provide new opportunities in disease prognosis and modulating the microbiota with precision to improve both short-term and long-term patient outcomes (SLAS Technology 2019 Link). 

 

Single cell analysis