RRC ID 85781
著者 Chakrabarty P, R A, Suzuki R, Vedantam S, Rao S, Nagai M, Santra TS.
タイトル High Throughput Intracellular Delivery Using a 2D Cell-Squeezing Mechanoporation Device and Its Analysis by a Deep Learning Model.
ジャーナル Adv Healthc Mater
Abstract Cell-squeezing mechanoporation is a simple method for intracellular delivery. Most studies rely on 1D constrictions; however, the throughput is limited. To enable parallel single-cell delivery with higher throughput, a 2D cell-squeezing microfluidic device is designed and fabricated. An array of vertical through-holes, 8 to 15-µm in diameter (up to 62 000 holes per device), is fabricated on a 20-µm thin SU-8 membrane integrated into a polydimethylsiloxane (PDMS) structure. Intracellular delivery occurred by diffusion as single-cells are rapidly sheared through the vertical constrictions. The platform demonstrates dextran (4-40-kDa) delivery into an adherent (HeLa) and a suspension (Jurkat) cell line at throughputs up to 3 million cells min-1. The device also delivers small interfering ribonucleic acid (siRNA) and plasmids into primary human mesenchymal stem cells (hMSCs) and human gingival fibroblasts (hGFs), validating its potential for therapeutic applications. The delivery results are analyzed using "image cytometry"-which combines instance segmentation with a rule-based image processing system. The automated evaluation of single-cell states, such as cell diameter and fluorescence intensity, is done, which evaluates cell diameter distribution and fluorescence variation within an experimental sample. Quantification of a mechanoporation-based intracellular delivery at a single-cell resolution is performed with an automated deep learning (DL)-based analysis system.
ページ e02472
公開日 2025-8-21
DOI 10.1002/adhm.202502472
PMID 40838383
IF 7.367
リソース情報
ヒト・動物細胞 HeLa(RCB0007)