Single-cell delivery platforms like microinjection and nanoprobe electroporation enable unparalleled control over cell manipulation tasks but are generally limited in throughput. Here, we present an automated single-cell electroporation system capable of automatically detecting cells with artificial intelligence (AI) software and delivering exogenous cargoes of different sizes with uniform dosage. We implemented a fully convolutional network (FCN) architecture to precisely locate the nuclei and cytosol of six cell types with various shapes and sizes, using phase contrast microscopy. Nuclear staining or reporter fluorescence was used along with phase contrast images of cells within the same field of view to facilitate the manual annotation process. Furthermore, we leveraged the near-human inference capabilities of the FCN network in detecting stained nuclei to automatically generate ground-truth labels of thousands of cells within seconds, and observed no statistically significant difference in performance compared to training with manual annotations. The average detection sensitivity and precision of the FCN network were 95±1.7% and 90±1.8%, respectively, outperforming a traditional image-processing algorithm (72±7.2% and 72±5.5%) used for comparison. To test the platform, we delivered fluorescent-labeled proteins into adhered cells and measured a delivery efficiency of 90%. As a demonstration, we used the automated single-cell electroporation platform to deliver Cas9–guide RNA (gRNA) complexes into an induced pluripotent stem cell (iPSC) line to knock out a green fluorescent protein–encoding gene in a population of ~200 cells. The results demonstrate that automated single-cell delivery is a useful cell manipulation tool for applications that demand throughput, control, and precision.

像显微注射和纳米探针电穿孔这样的单细胞递送平台能够对细胞操作任务进行无与伦比的控制，但通常在吞吐量方面受到限制。在这里，我们提出了一种自动化单细胞电穿孔系统，该系统能够使用人工智能 (AI) 软件自动检测细胞，并以统一的剂量输送不同大小的外源性货物。我们实施了一个完全卷积网络 (FCN) 架构，以使用相差显微镜精确定位六种不同形状和大小的细胞类型的细胞核和细胞质。核染色或报告荧光与同一视野内细胞的相差图像一起使用，以促进手动注释过程。此外，我们利用 FCN 网络近乎人类的推理能力来检测染色核，在几秒钟内自动生成数千个细胞的真实标签，并且与使用手动注释的训练相比，在性能上没有观察到统计学上的显着差异。FCN 网络的平均检测灵敏度和精度分别为 95±1.7% 和 90±1.8%，优于用于比较的传统图像处理算法（72±7.2% 和 72±5.5%）。为了测试该平台，我们将荧光标记的蛋白质输送到粘附的细胞中，并测量了 90% 的输送效率。作为示范，我们使用自动化单细胞电穿孔平台将 Cas9 引导 RNA (gRNA) 复合物递送到诱导多能干细胞 (iPSC) 系中，以敲除约 200 个细胞群中的绿色荧光蛋白编码基因。结果表明，自动化单细胞递送是一种有用的细胞操作工具，适用于需要吞吐量、控制和精度的应用。