Effects of acetic acid on light scattering from cells

Marina, Oana C.

doi:10.1117/1.jbo.17.8.085002

Cited by 17 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The observation that light scattering can indicate metastatic efficiency suggests that the cellular organelles and processes captured by light scattering are relevant to the metastatic process (Schürmann et al, 2015). Indeed, differences in light scattering upon acetic acid treatment are often used to detect cancerous cells in patients (Marina et al, 2012). Although the mechanisms underlying light scattering of cells are unclear, intracellular organelles such as phase separated droplets (Falke et al, 2019) or lysosomes will be detected by changes to light scattering (Choi et al, 2007).…”

Section: Interpretation Of Latent Features Discriminating High and Lomentioning

confidence: 99%

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Zaritsky

Jamieson

Welf

et al. 2020

Preprint

View full text Add to dashboard Cite

Deep convolutional neural networks have emerged as a powerful technique to identify hidden patterns in complex cell imaging data. However, these machine learning techniques are often criticized as uninterpretable "black-boxes" -lacking the ability to provide meaningful explanations for the cell properties that drive the machine's prediction. Here, we demonstrate that the latent features extracted from label-free live cell images by an adversarial auto-encoding deep convolutional neural network capture subtle details of cell appearance that allow classification of melanoma cell states, including the metastatic efficiency of seven patientderived xenograft models that reflect clinical outcome. Although trained exclusively on patientderived xenograft models, the same classifier also predicted the metastatic efficiency of immortalized melanoma cell lines suggesting that the latent features capture properties that are specifically associated with the metastatic potential of a melanoma cell regardless of its origin.We used the autoencoder to generate "in-silico" cell images that amplified the cellular features driving the classifier of metastatic efficiency. These images unveiled pseudopodial extensions and increased light scattering as functional hallmarks of metastatic cells. We validated this interpretation by analyzing experimental image time-lapse sequences in which melanoma cells spontaneously transitioned between states indicative of low and high metastatic efficiency.Together, this data is an example of how the application of Artificial Intelligence supports the identification of processes that are essential for the execution of complex integrated cell functions but are too subtle to be identified by a human expert. -Gurion University of the Negev, Israel (to AZ). We thank Sean Morrison for PDX-derived cell models. We thank Andrew R. Cohen for LEVER. Author ContributionAZ, ESW and GD conceived the study. ESW designed the experimental assay, optimized culture conditions for PDX cells, and trained AN and JC to perform the experiments. AN performed all live imaging and mouse experiments. JC performed additional experiments. UE generated the PDX samples. AZ and ARJ developed analytic tools, analyzed, and interpreted the data. AZ drafted the manuscript. All authors wrote and edited the manuscript and approved its content.GD mentored all authors.

show abstract

Section: Interpretation Of Latent Features Discriminating High and Lomentioning

confidence: 99%

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Zaritsky

Jamieson

Welf

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The colposcope׳s magnification and fiber-optic lighting along with application of 3–5% acetic acid and/or Lugol׳s staining result in recognizable epithelial and vascular changes or characteristics associated with cervical SIL that would otherwise be invisible. The mechanism of action by which acetowhitening occurs is incompletely understood but may be caused by light scattering in dysplastic tissues, osmotic shifts, and/or changes in cytokeratin expression causing lesions to turn white when acetic acid applied and can thus be recognized as potentially abnormal and targeted for biopsy [14] , [15] . Like acetic acid, Lugol׳s staining is used in cervical colposcopy to help differentiate cHSIL from cLSIL and to determine the lesion margins for treatment.…”

Section: Introductionmentioning

confidence: 99%

Colposcopic characteristics and Lugol׳s staining differentiate anal high-grade and low-grade squamous intraepithelial lesions during high resolution anoscopy

Jay

Berry

Miaskowski

et al. 2015

Papillomavirus Research

View full text Add to dashboard Cite

Background Anal squamous intraepithelial lesions (SIL) and cancers are increased in immunocompromised populations. Based upon anatomic and histologic similarities, the cervix is used as the model for anal screening. During cervical colposcopy, acetic acid (AA) and Lugol׳s staining (LS) result in characteristic changes that help distinguish low-grade (L)SIL from high-grade (H)SIL. Lesion characteristics were evaluated for their ability to distinguish anal (a)LSIL from anal (a)HSIL during high-resolution anoscopy after application of AA and LS. Methods AA-stained lesions were described using standard cervical colposcopic criteria. LS was then applied and lesions were characterized as Lugol׳s-negative (L−), Lugol׳s-partial (L+/−), or Lugol׳s positive (L+) and then biopsied. Biopsies were characterized as benign, squamous atypia, LSIL or HSIL. Results 835 anal lesions were analyzed. Sensitivity and positive predictive value (PPV) for aHSIL were highest for characteristics associated with cervical (c)HSIL. L− was independently associated with aHSIL (OR=4.7, 95% CI=3.4–6.7). In multiple logistic regression analysis, significant predictors of aHSIL were flat contour (OR=2.24, 95% CI=1.3–3.8), mosaic pattern (OR=2.0, 95% CI=1.4–2.9), vascular punctation (OR=1.5, 95% CI=1.1–2.1) and L− (OR=2.3, 95% CI=1.5–3.4). L− staining improved the PPV of aHSIL almost twofold in lesions that otherwise had a colposcopic impression of LSIL. Conclusions Evaluating acetowhite lesions for contour, surface, vascularity, and LS may maximize the likelihood of identifying aHSIL.

show abstract

“…More advanced fibre optic probes for PLSS made use of gradient-index (GRIN) lens to implement depth selective measurement from a range of near surface depths (100-200 mm) to interrogate blood supply [66,67]. A great number of translational studies for diagnosis of breast cancer [68], prostate cancer [69], intraoperative determination of sentinel lymph node status in the breast [70], colonic lesions [71], oral premalignancy [72], high grade dysplasia and cancer in Barrett's oesophagus [73,74], cervix neoplasia [75,76], investigation of the nature of aceto-whitening in cervix cancer inspection [77][78][79][80][81][82], gold nanoparticle sizing [83], and monitoring apoptosis in cell cultures [84][85][86] have demonstrated that LSS provides a promising solution for cell culture monitoring, tissue surveillance and biopsy guidance in a relatively simple and cost effective configuration.…”

Section: Polarized Light and Biological Tissuementioning

confidence: 99%

Mueller polarimetric imaging for surgical and diagnostic applications: a review

2017

View full text Add to dashboard Cite

Polarization is a fundamental property of light and a powerful sensing tool that has been applied to many areas. A Mueller matrix is a complete mathematical description of the polarization characteristics of objects that interact with light, and is known as a transfer function of Stokes vectors which characterise the state of polarization of light. Mueller polarimetric imaging measures Mueller matrices over a field of view and thus allows for visualising the polarization characteristics of the objects. It has emerged as a promising technique in recent years for tissue imaging, improving image contrast and providing a unique perspective to reveal additional information that cannot be resolved by other optical imaging modalities. This review introduces the basis of the Stokes-Mueller formulism, interpretation methods of Mueller matrices into fundamental polarization properties, polarization properties of biological tissues, and considerations in the construction of Mueller polarimetric imaging devices for surgical and diagnostic applications, including primary configurations, optimization procedures, calibration methods as well as the instrument polarization properties of several widelyused biomedical optical devices. The paper also reviews recent progress in Mueller polarimetric endoscopes and fibre Mueller polarimeters, followed by the future outlook in applying the technique to surgery and diagnostics. Tissue polarization properties convey morphological, micro-structural and compositional information of tissue with great potential for label free characterization of tissue pathological changes. Recent progress in tissue polarimetric imaging and polarization resolved endoscopy paved the way for translation of polarimetric imaging to surgery and tissue diagnosis.

show abstract

Effects of acetic acid on light scattering from cells

Cited by 17 publications

References 30 publications

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Colposcopic characteristics and Lugol׳s staining differentiate anal high-grade and low-grade squamous intraepithelial lesions during high resolution anoscopy

Mueller polarimetric imaging for surgical and diagnostic applications: a review

Contact Info

Product

Resources

About