Volume 18, Issue 20 2270105
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Deep Learning-Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard-to-Transfect Cells (Small 20/2022)

Prithvijit Mukherjee

Prithvijit Mukherjee

Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208 USA

Theoretical and Applied Mechanics Program, Northwestern University, Evanston, IL, 60208 USA

iNfinitesimal LLC, Skokie, IL, 60077 USA

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Cesar A. Patino

Cesar A. Patino

Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208 USA

iNfinitesimal LLC, Skokie, IL, 60077 USA

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Nibir Pathak

Nibir Pathak

Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208 USA

Theoretical and Applied Mechanics Program, Northwestern University, Evanston, IL, 60208 USA

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Vincent Lemaitre

Vincent Lemaitre

iNfinitesimal LLC, Skokie, IL, 60077 USA

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Horacio D. Espinosa

Horacio D. Espinosa

Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208 USA

Theoretical and Applied Mechanics Program, Northwestern University, Evanston, IL, 60208 USA

iNfinitesimal LLC, Skokie, IL, 60077 USA

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First published: 19 May 2022
Citations: 1

Graphical Abstract

Genetic Manipulation

In article number 2107795, Horacio D. Espinosa and co-workers present an automated nanopipette-based single cell electroporation system using arrays of microwells, a deep convolutional neural network for cell nuclei localization, and hardware and software for cell–nanopipette contact detection. The system enables CRISPR/Cas9 gene editing and cell perturbation studies with unprecedented throughput, efficiency, and cell viability. Applications include generation of isogenic cell lines, delivery of plasmids for reporter expression, and Cas9 ribonucleoprotein complexes for gene knockout.

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