Intelligent image-based deformation-assisted cell sorting with molecular specificity.
Animals
Cell Culture Techniques
Cell Line
Cell Proliferation
Cell Size
Cell Survival
Drosophila
/ cytology
Erythrocyte Deformability
Erythrocytes
/ cytology
Flow Cytometry
/ methods
HL-60 Cells
Humans
Microfluidics
/ methods
Myeloid Cells
/ cytology
Neural Networks, Computer
Neutrophils
/ cytology
Sound
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
06 2020
06 2020
Historique:
received:
15
10
2019
accepted:
09
04
2020
pubmed:
27
5
2020
medline:
12
9
2020
entrez:
27
5
2020
Statut:
ppublish
Résumé
Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves and transfer molecular specificity to image-based sorting using an efficient deep neural network. In addition to general performance, we demonstrate the utility of this method by sorting neutrophils from whole blood without labels.
Identifiants
pubmed: 32451476
doi: 10.1038/s41592-020-0831-y
pii: 10.1038/s41592-020-0831-y
doi:
Banques de données
figshare
['10.6084/m9.figshare.11302595']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
595-599Références
Dainiak, M. B., Kumar, A., Galaev, I. Y. & Mattiasson, B. in Cell Separation 1–18 (Springer, 2007).
Wyatt Shields, Iv,C., Reyes, C. D. & López, G. P. Microfluidic cell sorting: a review of the advances in the separation of cells from debulking to rare cell isolation. Lab Chip 15, 1230–1249 (2015).
doi: 10.1039/C4LC01246A
Baron, C. S. et al. Cell type purification by single-cell transcriptome-trained sorting. Cell 179, 527–542 (2019).
doi: 10.1016/j.cell.2019.08.006
Stamm, C. et al. Autologous bone-marrow stem-cell transplantation for myocardial regeneration. Lancet 361, 45–46 (2003).
doi: 10.1016/S0140-6736(03)12110-1
Bartsch, U. et al. Retinal cells integrate into the outer nuclear layer and differentiate into mature photoreceptors after subretinal transplantation into adult mice. Exp. Eye Res. 86, 691–700 (2008).
doi: 10.1016/j.exer.2008.01.018
Miltenyi, S., Müller, W., Weichel, W. & Radbruch, A. High gradient magnetic cell separation with MACS. Cytometry 11, 231–238 (1990).
doi: 10.1002/cyto.990110203
Bonner, W. A., Hulett, H. R., Sweet, R. G. & Herzenberg, L. A. Fluorescence activated cell sorting. Rev. Sci. Instrum. 43, 404–409 (1972).
doi: 10.1063/1.1685647
Shapiro, H. M. Practical Flow Cytometry (John Wiley & Sons, 2003).
Preira, P. et al. Passive circulating cell sorting by deformability using a microfluidic gradual filter. Lab Chip 13, 161–170 (2013).
doi: 10.1039/C2LC40847C
Wang, G. et al. Microfluidic cellular enrichment and separation through differences in viscoelastic deformation. Lab Chip 15, 532–540 (2015).
doi: 10.1039/C4LC01150C
Beech, J. P., Holm, S. H., Adolfsson, K. & Tegenfeldt, J. O. Sorting cells by size, shape and deformability. Lab Chip 12, 1048–1051 (2012).
doi: 10.1039/c2lc21083e
Otto, O. et al. Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat. Methods 12, 199–202 (2015).
doi: 10.1038/nmeth.3281
Toepfner, N. et al. Detection of human disease conditions by single-cell morpho-rheological phenotyping of blood. eLife 7, e29213 (2018).
doi: 10.7554/eLife.29213
Nitta, N. et al. Intelligent image-activated cell sorting. Cell 175, 266–276 (2018).
doi: 10.1016/j.cell.2018.08.028
Rosendahl, P. et al. Real-time fluorescence and deformability cytometry. Nat. Methods 15, 355 (2018).
doi: 10.1038/nmeth.4639
Nawaz, A. A. et al. Acoustofluidic fluorescence activated cell sorter. Anal. Chem. 87, 12051–12058 (2015).
doi: 10.1021/acs.analchem.5b02398
Girault, M. et al. An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution. Sci. Rep. 7, 40072 (2017).
doi: 10.1038/srep40072
Girardo, S. et al. Standardized microgel beads as elastic cell mechanical probes. J. Mater. Chem. B 6, 6245–6261 (2018).
doi: 10.1039/C8TB01421C
Mietke, A. et al. Extracting cell stiffness from real-time deformability cytometry: theory and experiment. Biophys. J. 109, 2023–2036 (2015).
doi: 10.1016/j.bpj.2015.09.006
Mokbel, M. et al. Numerical simulation of real-time deformability cytometry to extract cell mechanical properties. ACS Biomater. Sci. Eng. 3, 2913–2962 (2017).
doi: 10.1021/acsbiomaterials.6b00558
Hartono, D. et al. On-chip measurements of cell compressibility via acoustic radiation. Lab Chip 11, 4072–4080 (2011).
doi: 10.1039/c1lc20687g
Gustafson, M. P. et al. A method for identification and analysis of non-overlapping myeloid immunophenotypes in humans. PLoS One 10, e0121546 (2015).
doi: 10.1371/journal.pone.0121546
Bashant, K. R. et al. Real-time deformability cytometry reveals sequential contraction and expansion during neutrophil priming. J. Leukoc. Biol. 105, 1143–1153 (2019).
doi: 10.1002/JLB.MA0718-295RR
Di Carlo, D., Irimia, D., Tompkins, R. G. & Toner, M. Continuous inertial focusing, ordering, and separation of particles in microchannels. Proc. Natl Acad. Sci. USA 104, 18892–18897 (2007).
doi: 10.1073/pnas.0704958104
Ding, X. et al. Surface acoustic wave microfluidics. Lab Chip 13, 3626–3649 (2013).
doi: 10.1039/c3lc50361e
Bradski, G. The OpenCV library. Dr Dobb’s J. Softw. Tools 25, 120–126 (2000).
Herold, C. Mapping of deformation to apparent Young’s modulus in real-time deformability cytometry. Preprint at https://arxiv.org/abs/1704.00572 (2017).
Kräter, M. et al. AIDeveloper: deep learning image classification in life science and beyond. Preprint at https://doi.org/10.1101/2020.03.03.975250 (2020).
Nickolls, J., Buck, I., Garland, M. & Skadron, K. Scalable parallel programming with CUDA. Queue 6, 40 (2008).
doi: 10.1145/1365490.1365500
Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous dstributed systems. Preprint at https://arxiv.org/abs/1603.04467 (2016).
Al-Rfou, R. et al. Theano: a Python framework for fast computation of mathematical expressions. Preprint at https://arxiv.org/abs/1605.02688 (2016).
Glaubitz, M. et al. A novel contact model for AFM indentation experiments on soft spherical cell-like particles. Soft Matter 10, 6732–6741 (2014).
doi: 10.1039/C4SM00788C