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Cellular and Molecular Bioengineering

Cellular and Molecular Bioengineering - Poster Session E

Poster I12 - Ultrasound-Mediated Cell Transfection in Microchamber Systems

Saturday, October 26, 2024

10:00 AM - 11:00 AM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

Maria Steis

Undergraduate Student, dept. of Biomedical Engineering
Boston University
Arlington, Virginia, United States

Primary Investigator(s)

JK

Jonathan Kopechek, PhD

Associate Professor, Dept. of Bioengineering
University of Louisville
Louisville, Kentucky, United States

Co-Author(s)

MC

Madison Colebank

Graduate Student, dept. of Bioengineering
University of Louisville, United States
MA

Maria Abbinante

Graduate Student, dept. of Bioengineering
University of Louisville, United States
JM

John Moore

Graduate Student, dept. of Bioengineering
University of Louisville, United States

Introduction: In the realm of emerging cancer therapies, chimeric antigen receptor (CAR)-T cell therapy stands out for its potential to revolutionize cancer treatment. By harnessing and reprogramming a patient's white blood cells to target cancer cells, this therapy promises precision and efficacy against even the most resistant cancers. However, a current barrier with CAR-T cell therapy is the time spent genetically modifying T-cells in the lab.¹ The Kopechek lab aims to address this by optimizing cell transfection, or the artificial insertion of cancer-fighting genes into T-cells. Our approach utilizes ultrasound-driven microbubble oscillation to deliver genetic material into cells. Furthermore, we employ polydimethylsiloxane (PDMS) microchamber wells (Figure 1) to achieve a single-cell analysis of the transfection process. In this study, we aimed to optimize cell plating to achieve “one cell per well” and created a MATLAB computational model to analyze fluorescence-stitched microscopy images.

Materials and

Methods: To fabricate our microwells, we centrifuged and desiccated a 1:10 PDMS elastomer and curing agent solution and poured it into a SU8 mold of three different microwell sizes (20 µm, 50 µm, and 100 µm.) The chips were cured at 55° C, sprayed with 70% ethanol, cut out with a razor, and sterilized under UV in the biosafety cabinet. Each chip was then placed in 35 mm Petri dishes and coated with 0.4 mL of a Fibronection and phosphate buffer solution (PBS). The coated chips were incubated at 37° C and cleaned off before they either went to storage at 4 ° C or immediately to seeding. For seeding, we used a 5 mL Jurkat T-cell stock with RPMI medium. The seeded chips were then incubated at 37° C for at least 24 hours before transfecting or imaging. For fluorescence imaging, we stained the chips with Calcein AM diluted in 10x PBS. The chips were incubated for 15 minutes, cleaned, and flipped upside down in PBS for imaging on a BZ-X810 Fluorescence Microscope. We then ran the images through a MATLAB code that binarized the fluorescence image to count the number of cell clumps and sorted these clumps into labels of one-cell clump, two-cell clump, and three or more cell clumps based on clump size, aspect ratio, and circularity. It then created a grid marking the microchamber well boundaries and counted the number of cells per well.

Results, Conclusions, and Discussions: Our study characterized and optimized cell seeding parameters in microwells by comparing the number of single cells per well on different chips as found through our computational analysis. We first linked seeding concentration to well size. We found that at larger microwell sizes, a lower cell concentration is optimal and that seeding at 75,000 cells/mL in 50 μm wells yielded the best results (Figure 1). We also found an optimal imaging time after seeding of one day (Figure 2). We characterized the effect of bubbles due to PDMS aeration on cell seeding (Table 1) and increased desiccation time to minimize bubble formation. A study of cells being seeded over time showed that 92% of wells showed proliferation over three days (Figure 3). Finally, we found that under optimized seeding conditions, we could achieve up to 10% of wells with a single cell per well.
This study provides a proof of concept for single-cell transfection analysis in microchamber wells. Our computational microchamber well microscopy analysis model opens doors to more in-depth studies, paving the way for machine learning image processing models. This, in turn, can allow us to verify parameters for cell transfection, enabling us to predict the efficacy of transfection and pre-eliminate sources of error. This approach has applications in future sonoporation transfection studies in our lab involving single-cell analysis, which could lead to improved immunotherapies for cancer patients.

Acknowledgements (Optional): This research was supported by the National Science Foundation IMPACT-NG Undergraduate Research Experience under Grant No. 2348869 and KY Multiscale under Grant No.2025075. A special thanks to Jonathan Kopechek and the Kopechek lab, IMPACT-NG Program director Ana Galiano, Principal Investigator Kevin Walsh, and the University of Louisville J.B. Speed School of Engineering for research guidance and resources.