Sheneman, L. J. | Search bioRxiv

bioRxiv2024

Near-Zero Photon Bioimaging

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon-sparsity can be addressed by collecting more than 200 photons per pixel; this, however, requires extended acquisition durations and, thus, suboptimal imaging rates. Here, we introduce near-zero photon imaging, a method that operates at kHz rates and 10,000-fold lower irradiance than modern microscopy. To achieve this performance, we deployed a judiciously designed epi-fluorescence microscope enabling ultralow background and artificial intelligence that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon imaging captures the structure of both multicellular and subcellular targets with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon imaging paradigm can be applied in remote sensing, covert applications, and biological or biomedical imaging that utilize damaging or quantum light.

biophysics↗

bioRxiv2020

Deep Learning Classification of Lipid Droplets in Quantitative Phase Images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells. Author SummaryRecently, quantitative-phase imaging (QPI) has demonstrated the ability to elucidate novel parameters of cellular physiology and metabolism without the need for fluorescent staining. Here, we apply label-free, low photo-toxicity QPI to yeast cells in order to identify lipid droplets (LDs), an important organelle with key implications in human health and biofuel development. Because QPI yields low specificity, we explore the use of modern machine learning methods to rapidly identify intracellular LDs with high discriminatory power and accuracy. In recent years, machine learning has demonstrated exceptional abilities to recognize and segment objects in biomedical imaging, remote sensing, and other areas. Trained machine learning classifiers can be combined with QPI within high-throughput analysis pipelines, allowing for efficient and accurate identification and quantification of cellular components. Non-invasive, accurate and high-throughput classification of these organelles will accelerate research and improve our understanding of cellular functions with beneficial applications in biofuels, biomedicine, and more.

microbiology↗