Are droplets really suitable for single-cell analysis? A case study on yeast in droplets

Nakagawa, Yuta; Ohnuki, Shinsuke; Kondo, Naoko; Itto-Nakama, Kaori; Ghanegolmohammadi, Farzan; Isozaki, Akihiro; Ohya, Yoshikazu; Goda, Keisuke

doi:10.1039/d1lc00469g

Cited by 12 publications

(12 citation statements)

References 75 publications

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“…We see that cells grew more in the swollen and diluted DE droplets than in nonswollen droplets with standard media. This contradicts the flask results, although it is in agreement with other studies showing that the cell growth phase was delayed in smaller microfluidic droplets in comparison with larger droplets. − Our enhanced growth in swollen cores may be because the accumulation of biological waste products in smaller cores reduces growth more strongly than a lack of nutrients or nonideal salinity does in the swollen cores. Clearly, droplet-based cell cultivation requires extra consideration over bulk cultures.…”

Section: Resultssupporting

confidence: 81%

Tuneable Cell-Laden Double-Emulsion Droplets for Enhanced Signal Detection

et al. 2023

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Water-in-oil-in-water (w/o/w) or double-emulsion (DE) droplets have been widely used for cellular assays at a single-cell level because of their stability and biocompatibility. The oil shell of w/o/w droplets plays the role of a semipermeable membrane that allows substances with low molecular weight (e.g., water) to travel through but restricts those with high molecular weight (e.g., fluorescent biomarkers). Therefore, the core of DEs can be manipulated using osmosis, resulting in the shrinking or swelling of the core. Water leaves the inner aqueous phase to the outer phase via the oil shell when the osmotic pressure of the outer phase is higher than that in the inner phase, causing the shrinkage of DEs and vice versa. These processes can be achieved by transferring the DEs to hypertonic or hypotonic solutions. Manipulation of the core size of DEs can be beneficial to cellular assays. First, due to the selectivity of the oil shell of DEs, the concentration of biomarkers in the core increases when the inner aqueous phase is shrunk, resulting in the enhancement of biosignals. We demonstrate this by encapsulating the Bgl3 enzyme-secreting yeast with a substrate that displays fluorescence after hydrolyzation. In a second application, a single GFP-tagged yeast cell was encapsulated in DEs. After swelling the core of DEs, we observe that the larger core of DEs promotes cell growth compared to those with the smaller cores, leading to more intracellular proteins (green-fluorescent protein) for screening. These osmotic manipulations provide new tools for droplet-based biochemistry.

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Section: Resultssupporting

confidence: 81%

Tuneable Cell-Laden Double-Emulsion Droplets for Enhanced Signal Detection

et al. 2023

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“…As a result, selected cells may not behave the same way when scaled up for real-world applications as they did when cultured within these nanoliter-sized compartments. This behavior has been recently observed for yeast cells, where the size of the droplet affects overall cell morphology following culture ( 14 ). Scientists often need to perform further experiments and do additional genetic manipulation of selected strains to adapt them to scaled-up industrial cultures, a process that can take several months or years without guaranteed success.…”

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confidence: 62%

“…Previous studies demonstrated that droplet size affects the division of cells in microfluidic droplets surrounded by oil ( 31 , 32 ). Another recent study has demonstrated that yeast cells cultured in large flasks, large droplets, and small droplets differed morphologically ( 14 ). There may be several reasons for this phenomenon.…”

Section: Discussionmentioning

confidence: 99%

High-throughput selection of cells based on accumulated growth and division using PicoShell particles

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et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Production of high-energy lipids by microalgae may provide a sustainable energy source that can help tackle climate change. However, microalgae engineered to produce more lipids usually grow slowly, leading to reduced overall yields. Unfortunately, culture vessels used to select cells based on growth while maintaining high biomass production, such as well plates, water-in-oil droplet emulsions, and nanowell arrays, do not provide production-relevant environments that cells experience in scaled-up cultures (e.g., bioreactors or outdoor cultivation farms). As a result, strains that are developed in the laboratory may not exhibit the same beneficial phenotypic behavior when transferred to industrial production. Here, we introduce PicoShells, picoliter-scale porous hydrogel compartments, that enable >100,000 individual cells to be compartmentalized, cultured in production-relevant environments, and selected based on growth and bioproduct accumulation traits using standard flow cytometers. PicoShells consist of a hollow inner cavity where cells are encapsulated and a porous outer shell that allows for continuous solution exchange with the external environment. PicoShells allow for cell growth directly in culture environments, such as shaking flasks and bioreactors. We experimentally demonstrate that Chlorella sp., Saccharomyces cerevisiae, and Chinese hamster ovary cells, used for bioproduction, grow to significantly larger colony sizes in PicoShells than in water-in-oil droplet emulsions (P < 0.05). We also demonstrate that PicoShells containing faster dividing and growing Chlorella clonal colonies can be selected using a fluorescence-activated cell sorter and regrown. Using the PicoShell process, we select a Chlorella population that accumulates chlorophyll 8% faster than does an unselected population after a single selection cycle.

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“…Furthermore, we can employ various machine learning approaches to recognize, predict, understand, and obtain data/knowledge. In this way, parametric approaches can be applied in the future to perform correlation analyses to compare morphologies [ 28 ], classification analyses to distinguish categories based on morphology [ 29 ], prediction analyses to identify similar morphologies [ 30 ], factor analyses to explore potential common factors [ 31 ], analysis of morphological diversity for breeding purposes [ 32 , 33 ], and analysis of sources of bias in microfluidic cell culture research [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Assignment of unimodal probability distribution models for quantitative morphological phenotyping

2022

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Background Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results. Results Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box–Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions. Conclusions Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less.

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Are droplets really suitable for single-cell analysis? A case study on yeast in droplets

Abstract: Single-cell analysis has become one of the main cornerstones of biotechnology, inspiring the advent of various microfluidic compartments for cell cultivation such as microwells, microtrappers, microcapillaries, and droplets. A fundamental...

Cited by 12 publications

References 75 publications

Tuneable Cell-Laden Double-Emulsion Droplets for Enhanced Signal Detection

Tuneable Cell-Laden Double-Emulsion Droplets for Enhanced Signal Detection

High-throughput selection of cells based on accumulated growth and division using PicoShell particles

Assignment of unimodal probability distribution models for quantitative morphological phenotyping

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