| Literature DB >> 35479393 |
Ming Li1,2, Hangrui Liu3, Siyuan Zhuang1, Keisuke Goda4,5,6.
Abstract
The interrogation of single cells has revolutionised biology and medicine by providing crucial unparalleled insights into cell-to-cell heterogeneity. Flow cytometry (including fluorescence-activated cell sorting) is one of the most versatile and high-throughput approaches for single-cell analysis by detecting multiple fluorescence parameters of individual cells in aqueous suspension as they flow past through a focus of excitation lasers. However, this approach relies on the expression of cell surface and intracellular biomarkers, which inevitably lacks spatial and temporal phenotypes and activities of cells, such as secreted proteins, extracellular metabolite production, and proliferation. Droplet microfluidics has recently emerged as a powerful tool for the encapsulation and manipulation of thousands to millions of individual cells within pico-litre microdroplets. Integrating flow cytometry with microdroplet architectures surrounded by aqueous solutions (e.g., water-in-oil-in-water (W/O/W) double emulsion and hydrogel droplets) opens avenues for new cellular assays linking cell phenotypes to genotypes at the single-cell level. In this review, we discuss the capabilities and applications of droplet flow cytometry (DFC). This unique technique uses standard commercially available flow cytometry instruments to characterise or select individual microdroplets containing single cells of interest. We explore current challenges associated with DFC and present our visions for future development. This journal is © The Royal Society of Chemistry.Entities:
Year: 2021 PMID: 35479393 PMCID: PMC9034116 DOI: 10.1039/d1ra02636d
Source DB: PubMed Journal: RSC Adv ISSN: 2046-2069 Impact factor: 4.036
Fig. 1Droplet flow cytometry enables a wide range of high-throughput single-cell analysis, by integrating emulsion microdroplets (e.g., water-in-oil-water double emulsion and hydrogel droplets) with commercially available flow cytometry.
Fig. 2Droplet flow cytometry for single-cell cultivation. (A) Schematic workflow of screening and selecting microalgal cells with high biomass and lipid production rate. (1) Encapsulation of single microalgal cells within gelatin droplets, (2) cultivation and metabolite accumulation of single cells, (3) transfer of cell-laden hydrogel droplets from oil into an aqueous phase after gelation, (4) staining of target metabolites in hydrogel beads, (5) screening and sorting of hydrogel beads containing a high level of metabolites, (6) recovery of cells from the hydrogel beads, (7) regrowth of released cells, which can be reintroduced into iterative selection. Adapted with permission from ref. 79. Copyright 2018 John Wiley & Sons, Inc. (B) Schematic diagram of the activity/sensitivity spectrum assessment of a highly heterogeneous bacteria population (microbiota) using DEs. Individual cells from microbital samples were encapsulated in DEs together with different concentrations of the antibiotic, amicoumacin A (Ami). After cultivation, DEs were stained for metabolic activity, selected by FACS, and analysed by next-generation sequencing (NGS) and bioinformatics. Adapted with permission from ref. 31. Copyright 2018 National Academy of Sciences.
Fig. 3Droplet flow cytometry for molecular evolution. (A) Schematic workflow of directed enzyme evolution by screening and selecting positive DE droplets via FACS. Left: (1) a variant gene library was transformed and cloned into E. coli, (2) the encoded proteins translated within E. coli, (3) encapsulation of single cells in the W/O emulsion droplets, (4) addition of fluorogenic substrates through the oil phase and the formation of W/O/W DE droplets. Cells with functional enzymes can convert the non-fluorescence substrates into fluorescent products entrapped in the internal core, (5) analysis of enzymatic activity across the DE droplet populations via FACS. Right: FACS results for DE droplets containing E. coli expressing wide-type (wt) enzyme, unselected library (R0) and the library after one (R1), two (R2) and three (R3) rounds of sorting based on gate M1. Adapted with permission from ref. 54. Copyright 2006 Cell Press. (B) Schematic workflow of directed enzyme evolution using gel-shell beads (GSBs) and FACS. (1) Encapsulation of single E. coli cells expressing target enzyme in single emulsions and cell lysis to liberate the enzyme and its coding plasmid, (2) release of a fluorescent product by catalysis, (3) formation of GSB to entrap products, (4) high-throughput screening and sorting of GSB containing catalytically active hits via FACS, (5) recovery of variants with desired phenotypes, (6) iterative rounds of selection. Adapted with permission from ref. 93. Copyright 2014 Nature Publishing Group.
Fig. 4Droplet flow cytometry for single-cell detection. (A) Schematic diagram of rare pathogen detection by droplet-based PCR using agarose droplets and FACS. The rare pathogens (O157) and high-density normal bacteria (K12) are co-encapsulated into agarose droplets for PCR amplification. The PCR reagent mixture including two forwards primers labelled fluorescent dyes and two specific reverse primers specific for K12 and O157, respectively, were covalently conjugated to agarose. After PCR and cooling down, the agarose beads are analysed by flow cytometry for the detection of O157 cells. Adapted with permission from ref. 27. Copyright 2012 Royal Society of Chemistry. (B) Schematic workflow of the detection of cytokines secreted by single cells using agarose droplets and FC. Single Jurkat T cells were encapsulated within agarose droplets together with functionalized cytokine-capture nanobeads. After incubation, gelation, demulsification and washing, agarose beads were stained with fluorescent antibodies which can bind to the secreted cytokines, and high-throughput screening of cytokines secreted by single cells were performed by flow cytometry. Adapted with permission from ref. 25. Copyright 2013 Royal Society of Chemistry.
Fig. 5Droplet flow cytometry for cell-to-cell interaction. (A) Screening of bacteria inhibiting S. aureus growth using DE-FACS. (1) Target S. aureus cells with a GFP reporter were encapsulated with either antibiotic producer, S. venezuelae (secreting red fluorescent metabolites) or mate E. coli (with a far-red fluorescent reporter). (2) The inhibition of S. aureus with the growth of S. venezuelae, and the growth of both E. coli and S. aureus, which yielded different combinations of fluorescent signals. (3 and 4) Selection of DE droplets with the lowest fluorescence level caused the enrichment of killers S. venezuelae (3) rather than mates E. coli (4). Adapted with permission from ref. 32. Copyright 2017 National Academy of Sciences. (B) The workflow of high-throughput screening and selection of the co-culture of recombinant E. coli and S. aureus for antibiotic drug discovery via the integration of agarose droplets and FACS. (1) environmental DNA was subjected to a limited DNasel digestion, (2) metagenomic DNA was cloned and then transferred into E. coli, (3) individual recombinant E. coli were co-encapsulated with live S. aureus (small yellow spheres), (4) microdroplets were labelled with a fluorogenic viability probe and then selected by FACS to isolate E. coli secreting bacterial natural products. Adapted with permission from ref. 33. Copyright 2013 John Wiley & Sons, Inc.
Summary of capabilities of droplet flow cytometry for high-throughput single-cell analysis
| Microdroplet | Target cell | Application |
|---|---|---|
|
| ||
| W/O/W |
| Cell culture (from proliferation to death)[ |
| W/O/W | Oral microbiota of Siberian bear | Cell culture (with antibiotics)[ |
| Human fecal microbiota from patient and healthy donors | ||
| W/O/W | Mouse embryonic stem cells (E14), macrophage cells (LM-1) and embryonic fibroblasts (NIH 3T3); human T lymphocytes (Jurkat) and embryonic kidney cells (HEK 239T) | Screening of cell encapsulation ratios for different types of cells[ |
| Gelatin |
| Chlorophyll and lipid accumulation[ |
| Agarose |
| MIC of rifampicin determination; mutant isolation[ |
|
| ||
| W/O/W |
| Thiolactonases (100 fold increase)[ |
| Arysulfatase[ | ||
| β-Glucosidase (∼2 fold increase for lactose)[ | ||
| G-type nerve agent hydrolase (104-fold increase)[ | ||
| Cutinase (8 fold increase)[ | ||
| Esterase (2 fold increase)[ | ||
| Polymerase (1200 fold increase)[ | ||
| W/O/W |
| Protease (1.6 fold increase)[ |
| W/O/W |
| Endoglucanase-II cellulase (20 fold increase)[ |
| Glucose oxidase (5.8 fold increase)[ | ||
| Agarose-alginate polyelectrolyte |
| Phosphotriesterase (20 fold increase)[ |
| PEG |
| Hydrolytic enzyme (3 fold increase)[ |
| W/O/W |
| Riboflavin (54 fold increase)[ |
| W/O/W |
| Lactic acid (52% more effective)[ |
|
| ||
| Agarose |
| Detection of O157:H7 (0.0001%)[ |
| Agarose | Human gastric carcinoma cells (Kato III) and breast cancer cells (MDA-MB-231) | Differentiation in gene expression level of a cancer biomarker (EpCAM)[ |
| Polyacrylamide-agarose |
| Differentiation of a tetracycline resistance gene[ |
| Agarose |
| Secretion of cytokines: IFN-γ, IL-4, IL-10 or TGF-β[ |
| Secretion of cytokines: IL-2, IFN-γ, TNF-α[ | ||
|
| ||
| W/O/W |
| Antibiotic producer discovery[ |
|
| ||
|
| ||
| W/O/W | Oral microbiota of Siberian bear + | Antibiotic Ami discovery[ |
| Agarose |
| Lytic hydrolase producer for |
| Agarose |
| Viable bacteria improving algal growth[ |
| Agarose | Stromal cells (MBA2) + leukemia cells (M07e) | Impacts on M07e cells survival[ |
| Agarose |
| Cytokine secretion by |
| Agarose |
| Antibody secretion by |