One of the ultimate goals of medical science is to develop personalized diagnoses and treatments for disease. With a patient’s genetic information, doctors could tailor treatments to individuals, leading to safer and more effective care.
Recent work by a team of Northwestern Engineering researchers has brought the field one step closer to realizing that future.
Led by the teacher Horace Espinosa, the research team has developed a new version of its Nanofountain Probe Electroporation (NFP-E), a tool used to deliver molecules into individual cells using electricity. The improved method leverages artificial intelligence (AI) to perform cell engineering tasks such as locating cell nuclei and detecting probes. Other processes such as probe movement, probe-cell contact detection, and electroporation delivery of foreign cargo into individual cells are also automated, minimizing user intervention.
“NFP-E can handle small starting samples without any significant cell loss throughout the protocol,” said Espinosa, James N., and Nancy J. Farley, Professor of Manufacturing and Entrepreneurship at the McCormick School of Engineering and corresponding author of the study. “This is an advantage over other cell engineering methods such as bulk electroporation, which require millions of cells and result in significant cell loss. Automated NFP-E, combined with its ability to target and to selectively manipulate individual cells into microarrays, can be useful in basic research, such as deciphering intracellular dynamics and cell-to-cell communication studies as well as biological applications such as cell line generation.
Espinosa and graduate students Prithvijit Mukherjee, Cesar A. Patino, and Nibir Pathak reported their work in the paper “Deep Learning Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard-to-Transfect Cells” published March 21 in Small.
“Genetic manipulation of human induced pluripotent stem cells (hiPSCs) by introducing exogenous cargo has a wide range of applications in disease diagnosis, therapeutic discovery and regenerative medicine,” said Mukherjee, a PhD student from the Espinosa group who joins the microfluidics group. at Illumina.
Probe-based microfluidic methods, such as NFP-E, use hollow nanopipettes or atomic force microscopy tips to deliver materials into cells. NFP-E also allows researchers to selectively manipulate cells of interest, work with very small starting samples, and deliver both proteins and plasmids in a variety of animal and human cell types with control of Dosage.
“The challenge with probe methods, however, is that they require manual operation and produce low throughputs, which makes them unsuitable for common cell engineering workflows,” said Patino, a PhD student in the Espinosa group. .
“Selectively manipulating cells at sufficient throughput is challenging,” Espinosa said. “Most methods either provide high throughput at the expense of single cell monitoring or sacrifice throughput for single cell selectivity and monitoring.”
This new work changes that.
The research team’s automated NFP-E enables selective cell engineering at higher throughputs than manual probe-based methods while reducing experimental variability and enabling more efficient engineering of hiPSCs. Using the automated NFP-E platform, Espinosa and colleagues delivered regularly spaced short palindromic repeats (CRISPR) RNPs to hiPSCs for efficient gene knockout in a variety of culture formats: culture plates, micro-patterns and arrays of microwells. Automated cell engineering in microarrays using NFP-E has potential applications such as the generation of isogenic cell lines from single cells and the study of dynamic cellular processes such as intracellular signaling cascades and cell communication. -cell.
Espinosa and his team will then work to automate the entire NFP-E workflow, which includes steps such as automated cell imaging, cell tracking, probe switching, and media exchange for culture. cellular.
“The idea is to establish a fully automated cell line generation workflow using the combination of NFP-E and microwell arrays,” Espinosa said. “AI can be further trained to recognize and target specific cell types in multi-cell co-cultures. This can be useful for understanding dynamics such as disease progression or cell communication.
The research was supported by two NIH grants, award numbers 1R43GM128500-01 and 1R21GM132709-01.
The title of the article
Deep Learning-Assisted Automated Single-Cell Electroporation Platform for Efficient Genetic Manipulation of Difficult-to-Transfect Cells
Publication date of articles
March 21, 2022
Warning: AAAS and EurekAlert! are not responsible for the accuracy of press releases posted on EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.