Single-cell Printer: Automated, on Demand, and Label Free
Overview
Authors
Affiliations
Within the past years, single-cell analysis has developed into a key topic in cell biology to study cellular functions that are not accessible by investigation of larger cell populations. Engineering approaches aiming to access single cells to extract information about their physiology, phenotype, and genotype at the single-cell level are going manifold ways, meanwhile allowing separation, sorting, culturing, and analysis of individual cells. Based on our earlier research toward inkjet-like printing of single cells, this article presents further characterization results obtained with a fully automated prototype instrument for printing of single living cells in a noncontact inkjet-like manner. The presented technology is based on a transparent microfluidic drop-on-demand dispenser chip coupled with a camera-assisted automatic detection system. Cells inside the chip are detected and classified with this detection system before they are expelled from the nozzle confined in microdroplets, thus enabling a "one cell per droplet" printing mode. To demonstrate the prototype instrument's suitability for biological and biomedical applications, basic experiments such as printing of single-bead and cell arrays as well as deposition and culture of single cells in microwell plates are presented. Printing efficiencies greater than 80% and viability rates about 90% were achieved.
Thermal bubble single-cell printing chip: High-throughput, wide-field, and efficient.
Deng B, Wang K, Huang P, Yang M, Liu D, Guan Y Biomicrofluidics. 2024; 18(6):064102.
PMID: 39620057 PMC: 11604098. DOI: 10.1063/5.0225883.
Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics.
Guo Z, Li F, Li H, Zhao M, Liu H, Wang H Adv Sci (Weinh). 2024; 12(1):e2408353.
PMID: 39497614 PMC: 11906218. DOI: 10.1002/advs.202408353.
Data acquisition approaches for single cell proteomics.
Ghosh G, Shannon A, Searle B Proteomics. 2024; 25(1-2):e2400022.
PMID: 39088833 PMC: 11735665. DOI: 10.1002/pmic.202400022.
Shan T, Wu X, Hu Y, Lin X, Sun D Micromachines (Basel). 2023; 14(6).
PMID: 37374852 PMC: 10302590. DOI: 10.3390/mi14061267.
Carius P, Jungmann A, Bechtel M, Grissmer A, Boese A, Gasparoni G Adv Sci (Weinh). 2023; 10(8):e2207301.
PMID: 36748276 PMC: 10015904. DOI: 10.1002/advs.202207301.