BackgroundMost quantitative measures of phenotypic traits represent macroscopic contributions of large numbers of cells. Yet, cells of a tissue do not behave similarly, and molecular studies on several organisms have shown that regulations can be highly stochastic, sometimes generating diversified cellular phenotypes within tissues. Phenotypic noise, defined here as trait variability among isogenic cells of the same type and sharing a common environment, has therefore received a lot of attention. Given the potential fitness advantage provided by phenotypic noise in fluctuating environments, the possibility that it is directly subjected to evolutionary selection is being considered. For selection to act, phenotypic noise must differ between contemporary genotypes. Whether this is the case or not remains, however, unclear because phenotypic noise has very rarely been quantified in natural populations.ResultsUsing automated image analysis, we describe here the phenotypic diversity of S. cerevisiae morphology at single-cell resolution. We profiled hundreds of quantitative traits in more than 1,000 cells of 37 natural strains, which represent various geographical and ecological origins of the species. We observed abundant trait variation between strains, with no correlation with their ecological origin or population history. Phenotypic noise strongly depended on the strain background. Noise variation was largely trait-specific (specific strains showing elevated noise for subset of traits) but also global (a few strains displaying elevated noise for many unrelated traits).ConclusionsOur results demonstrate that phenotypic noise does differ quantitatively between natural populations. This supports the possibility that, if noise is adaptive, microevolution may tune it in the wild. This tuning may happen on specific traits or by varying the degree of global phenotypic buffering.
The upgraded version of intelligent image-activated cell sorting (iIACS) has enabled higher-throughput and more sensitive intelligent image-based sorting of single live cells from heterogeneous populations.
Droplet microfluidics has become a powerful tool in precision medicine, green biotechnology, and cell therapy for single-cell analysis and selection by virtue of its ability to effectively confine cells. However, there remains a fundamental trade-off between droplet volume and sorting throughput, limiting the advantages of droplet microfluidics to small droplets (<10 pl) that are incompatible with long-term maintenance and growth of most cells. We present a sequentially addressable dielectrophoretic array (SADA) sorter to overcome this problem. The SADA sorter uses an on-chip array of electrodes activated and deactivated in a sequence synchronized to the speed and position of a passing target droplet to deliver an accumulated dielectrophoretic force and gently pull it in the direction of sorting in a high-speed flow. We use it to demonstrate large-droplet sorting with ~20-fold higher throughputs than conventional techniques and apply it to long-term single-cell analysis of Saccharomyces cerevisiae based on their growth rate.
By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopygrade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s −1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.
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