Proceedings of the National Academy of Sciences · 2019 · 213 citations · 39 references
Natural SciencesCancer BiologyCell BiologyBiomedical OpticsBiophotonicsPhotonicsCellular ImagingFlow CytometryFluorescence ImagingFluorescence MicroscopyLabel-free ImagingMicrofluidicsDigital Image AnalysisHigh-throughput Cell ImagingMetabolic State HeterogeneityMicrofluidic AnalysisRaman ImagingRaman MicroscopyRaman Microscopy Of CellsStimulated Raman Scattering Microscopy
Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.
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Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
Sustainable development goals for people and planet
David Griggs, Mark Stafford‐Smith, Owen Gaffney et al. · Nature · 2013 · 2.8K citations
Social Sciences, Environmental Policy, Sustainability Management +12