Cell-to-cell influence on growth in large populations

Kandel, Mikhail E.; Lu, Wenlong; Liang, Jon; Aydin, Onur; Saif, Taher A.; Popescu, Gabriel

doi:10.1364/boe.10.004664

Cited by 18 publications

(20 citation statements)

References 46 publications

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“…The interference is created by waves propagating on a nearly identical path, which confers SLIM subangstrom path-length temporal sensitivity ( 37 ). Owing to these features and complete automation of data acquisition, SLIM can be used to study cell behavior of long periods of time and large fields of view across microscope slides and multiwell plates ( 18 , 34 ). To demonstrate the potential for SLIM to be used in a high-throughput setting, we imaged a whole microscope slide prepared according to Materials and Methods .…”

Section: Resultsmentioning

confidence: 99%

“…Following the procedure outlined in ref. 18, the dry mass of the cellular structures can be integrated within the margin's given by our computationally generated labels to yield the total dry mass for the three cellular compartments. The dry mass for these elements can then be related to the known fertility outcomes for the sperm sample from which they were drawn.…”

Section: Deep Learning For Semantic Segmentationmentioning

confidence: 99%

“…This quantitative phase image is linearly related to the dry-mass density of the cell under investigation ( 13 – 15 ). As a result, QPI has been applied successfully to studying cell-mass transport and growth ( 16 – 20 ). Furthermore, acquiring QPI data over a third axis, e.g., axially, angularly, spectrally resolved, allows us to extract tomographic reconstructions of unlabeled cells ( 21 – 25 ).…”

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confidence: 99%

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Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure

Kandel

Rubessa

et al. 2020

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

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The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.

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Section: Resultsmentioning

confidence: 99%

Section: Deep Learning For Semantic Segmentationmentioning

confidence: 99%

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confidence: 99%

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Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure

Kandel

Rubessa

et al. 2020

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

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“…Mie-scattering theory is used for the study of LaAsO4:Eu 3+ phosphor scattering effects and the computation of the scattering cross section Csca for spherical particles as expressed in the following expression [22,23]. Meanwhile, the Lambert-Beer law is used to compute the transmitted light power [24,25]:…”

Section: Resultsmentioning

confidence: 99%

Study of red-emitting LaAsO4:Eu3+ phosphor for color rendering index improvement of WLEDs with dual-layer remote phosphor geometry

2020

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The remote phosphor structure is often disadvantageous in color quality but in terms of optics it is more convenient when compared to other phosphor structures such as conformal or in-cup ones. From this disadvantage, there are many studies to improve its chromatic output. In this research paper, we propose a dual-layer remote phosphor geometry for the improvement of white light-emitting diodes (WLEDs) in two parameters: color rendering index (CRI) and color quality scale (CQS). The 7700 K WLEDs are used in this study. The idea of the study is to place a red phosphor layer LaAsO4:Eu 3+ on the yellow phosphor YAG: Ce 3+ , and then find the concentration of LaAsO4:Eu 3+ in an appropriate way to achieve the highest color quality. The results showed that LaAsO4:Eu 3+ bring great benefits for enhancing CRI and CQS. In particular, the greater the concentration of LaAsO4:Eu 3+ , the greater the CRI and CQS because the portion of red lights in WLEDs increases. However, the decrease in lumen output occurs when the concentration of LaAsO4:Eu 3+ increases excessively. This is proved thanks to Mie-scattering theory and Beer-Lambert law. The results of important articles in WLEDs fabrication have greatly contributed to a higher white light quality

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“…In addition, due to the shared optical path between QPI and fluorescence, we generate all the PICS training data completely automatically. We applied deep learning to QPI data, generated by SLIM (spatial light interference microscopy) [31][32][33][34] and GLIM (gradient light interference microscopy) 35,36 . These methods are white light and common path and, thus, provide high spatial and temporal sensitivity [37][38][39][40][41][42][43][44] .…”

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confidence: 99%

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

et al. 2020

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Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

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Cell-to-cell influence on growth in large populations

Cited by 18 publications

References 46 publications

Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure

Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure

Study of red-emitting LaAsO4:Eu3+ phosphor for color rendering index improvement of WLEDs with dual-layer remote phosphor geometry

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

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