Single-Cell Printer: Automated, On Demand, and Label Free

被引:89
|
作者
Gross, Andre [1 ]
Schoendube, Jonas [1 ]
Niekrawitz, Sonja [1 ]
Streule, Wolfgang [2 ]
Riegger, Lutz [2 ]
Zengerle, Roland [1 ,3 ]
Koltay, Peter [1 ]
机构
[1] Univ Freiburg, Lab MEMS Applicat, Dept Microsyst Engn IMTEK, D-79110 Freiburg, Germany
[2] BioFluidix GmbH, Freiburg, Germany
[3] HSG IMIT Inst Mikro & Informat Tech, Freiburg, Germany
来源
JALA | 2013年 / 18卷 / 06期
关键词
single cells; single-cell analysis; piezoelectric printing; single-cell microarrays; label free; FIBROBLASTS; VIABILITY; SYSTEM;
D O I
10.1177/2211068213497204
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:504 / 518
页数:15
相关论文
共 50 条
  • [41] Droplet microfluidics and deep learning for label-free analysis of single-cell bacterial growth and lysis
    Tiwari, Anuj
    Anagnostidis, Vasilis
    Nikolic, Nela
    Gielen, Fabrice
    EUROPEAN BIOPHYSICS JOURNAL WITH BIOPHYSICS LETTERS, 2023, 52 (SUPPL 1): : S110 - S110
  • [42] High-throughput analysis at single-cell level through multimodal label-free microscopy
    Pavillon, Nicolas
    Hobro, Alison J.
    Smith, Nicholas, I
    QUANTITATIVE PHASE IMAGING V, 2019, 10887
  • [43] A high-throughput sample preparation approach for label-free and multiplexed single-cell proteomics
    Ctortecka, Claudia
    Jacome, Alvaro Sebastian Vaca
    Vashist, Tanmayi
    Satpathy, Shankha
    Udeshi, Namrata D.
    Carr, Steven A.
    MOLECULAR & CELLULAR PROTEOMICS, 2022, 21 (08) : S18 - S18
  • [44] Label-free single-cell separation and imaging of cancer cells using an integrated microfluidic system
    Maria Antfolk
    Soo Hyeon Kim
    Saori Koizumi
    Teruo Fujii
    Thomas Laurell
    Scientific Reports, 7
  • [45] Label-Free Multivariate Biophysical Phenotyping-Activated Acoustic Sorting at the Single-Cell Level
    Li, Peixian
    Ai, Ye
    ANALYTICAL CHEMISTRY, 2021, 93 (08) : 4108 - 4117
  • [46] Label-free multivariate biophysical phenotyping-activated acoustic sorting at the single-cell level
    Ai, Ye (aiye@sutd.edu.sg), 1600, American Chemical Society (93):
  • [47] Scanning Electrochemical Photometric Sensors for Label-Free Single-Cell Imaging and Quantitative Absorption Analysis
    Wang, Jian
    Tian, Yulan
    Chen, Fangming
    Chen, Wei
    Du, Liping
    He, Zhiyuan
    Wu, Chunsheng
    Zhang, De-Wen
    ANALYTICAL CHEMISTRY, 2020, 92 (14) : 9739 - 9744
  • [48] Label-free single-cell live imaging reveals fast metabolic switch in T lymphocytes
    Paillon, Noemie
    Ung, Thi Phuong Lien
    Dogniaux, Stephanie
    Stringari, Chiara
    Hivroz, Claire
    BIOPHYSICAL JOURNAL, 2024, 123 (03) : 555A - 556A
  • [49] Label-free active single-cell encapsulation enabled by microvalve-based on-demand droplet generation and real-time image processing
    Wang, Yiming
    Wang, Yousu
    Wang, Xiaojie
    Sun, Wei
    Yang, Fengrui
    Yao, Xuebiao
    Pan, Tingrui
    Li, Baoqing
    Chu, Jiaru
    TALANTA, 2024, 276
  • [50] Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics
    Kovacs, Kinga Dora
    Beres, Balint
    Kanyo, Nicolett
    Szabo, Balint
    Peter, Beatrix
    Bosze, Szilvia
    Szekacs, Inna
    Horvath, Robert
    SCIENTIFIC REPORTS, 2024, 14 (01):