The Experts below are selected from a list of 26967 Experts worldwide ranked by ideXlab platform
Gabriel Popescu - One of the best experts on this subject based on the ideXlab platform.
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monitoring reactivation of latent hiv by label free gradient light Interference Microscopy
iScience, 2021Co-Authors: Neha Goswami, Mikhail E Kandel, Michael J Fanous, Kathrin Bohnwippert, Erin N Tevonian, Roy D Dar, Gabriel PopescuAbstract:Summary Human immunodeficiency virus (HIV) can infect cells and take a quiescent and nonexpressive state called latency. In this study, we report insights provided by label-free, gradient light Interference Microscopy (GLIM) about the changes in dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that the mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for latent cells that reactivate than those of the cells that did not reactivate. Cells with mean dry mass and diameter less than approximately 10 pg and 8 μm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than approximately 28-30 pg and mean diameter greater than 11–12 μm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells.
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spatial light Interference Microscopy slim principle and applications to biomedicine
Advances in Optics and Photonics, 2021Co-Authors: Xi Chen, Mikhail E Kandel, Gabriel PopescuAbstract:In this paper, we review spatial light Interference Microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike’s phase-contrast Microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.
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computational Interference Microscopy enabled by deep learning
APL Photonics, 2021Co-Authors: Mikhail E Kandel, Yuheng Jiao, Xiaojun Liu, Gabriel PopescuAbstract:Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light Interference Microscopy (SLIM) is a highly sensitive QPI method due to its partially coherent illumination and common path interferometry geometry. However, SLIM’s acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods such as diffraction phase Microscopy (DPM) allow for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, and high-sensitivity phase maps from DPM using single-shot images as the input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. The average peak signal-to-noise ratio, Pearson correlation coefficient, and structural similarity index measure were 29.97, 0.79, and 0.82 for the test dataset. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational Interference Microscopy imaging using blood smears, as they contain both erythrocytes and leukocytes, under static and dynamic conditions.
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computational Interference Microscopy enabled by deep learning
arXiv: Image and Video Processing, 2020Co-Authors: Mikhail E Kandel, Yuheng Jiao, Xiaojun Liu, Gabriel PopescuAbstract:Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light Interference Microscopy (SLIM) is a highly sensitive QPI method, due to its partially coherent illumination and common path interferometry geometry. However, its acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods like diffraction phase Microscopy (DPM), allows for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, high-sensitivity phase maps from DPM, single-shot images as input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules, such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1,000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational Interference Microscopy (CIM) imaging using blood smears, as they contain both erythrocytes and leukocytes, in static and dynamic conditions.
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monitoring reactivation of latent hiv by label free gradient light Interference Microscopy
bioRxiv, 2020Co-Authors: Neha Goswami, Mikhail E Kandel, Michael J Fanous, Kathrin Bohnwippert, Erin N Tevonian, Roy D Dar, Gabriel PopescuAbstract:Latent human immunodeficiency virus (HIV) reservoirs in infected individuals present the largest barrier to a cure. The first step towards overcoming this challenge is to understand the science behind latency-reactivation interplay. Fluorescence imaging of GFP-tagged HIV has been the main method for studying reactivation of latent HIV in individually infected cells. In this paper, we report insights provided by label-free, gradient light Interference Microscopy (GLIM) about the changes in measures including dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for cells with reactivated HIV as compared to those with latent disease. Results also indicate that cells with mean dry mass and diameter less than 10pg and 8μm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than 23pg and mean diameter greater than 11μm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells based on changes in cell morphology.
Mikhail E Kandel - One of the best experts on this subject based on the ideXlab platform.
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monitoring reactivation of latent hiv by label free gradient light Interference Microscopy
iScience, 2021Co-Authors: Neha Goswami, Mikhail E Kandel, Michael J Fanous, Kathrin Bohnwippert, Erin N Tevonian, Roy D Dar, Gabriel PopescuAbstract:Summary Human immunodeficiency virus (HIV) can infect cells and take a quiescent and nonexpressive state called latency. In this study, we report insights provided by label-free, gradient light Interference Microscopy (GLIM) about the changes in dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that the mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for latent cells that reactivate than those of the cells that did not reactivate. Cells with mean dry mass and diameter less than approximately 10 pg and 8 μm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than approximately 28-30 pg and mean diameter greater than 11–12 μm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells.
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spatial light Interference Microscopy slim principle and applications to biomedicine
Advances in Optics and Photonics, 2021Co-Authors: Xi Chen, Mikhail E Kandel, Gabriel PopescuAbstract:In this paper, we review spatial light Interference Microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike’s phase-contrast Microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.
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computational Interference Microscopy enabled by deep learning
APL Photonics, 2021Co-Authors: Mikhail E Kandel, Yuheng Jiao, Xiaojun Liu, Gabriel PopescuAbstract:Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light Interference Microscopy (SLIM) is a highly sensitive QPI method due to its partially coherent illumination and common path interferometry geometry. However, SLIM’s acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods such as diffraction phase Microscopy (DPM) allow for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, and high-sensitivity phase maps from DPM using single-shot images as the input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. The average peak signal-to-noise ratio, Pearson correlation coefficient, and structural similarity index measure were 29.97, 0.79, and 0.82 for the test dataset. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational Interference Microscopy imaging using blood smears, as they contain both erythrocytes and leukocytes, under static and dynamic conditions.
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computational Interference Microscopy enabled by deep learning
arXiv: Image and Video Processing, 2020Co-Authors: Mikhail E Kandel, Yuheng Jiao, Xiaojun Liu, Gabriel PopescuAbstract:Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light Interference Microscopy (SLIM) is a highly sensitive QPI method, due to its partially coherent illumination and common path interferometry geometry. However, its acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods like diffraction phase Microscopy (DPM), allows for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, high-sensitivity phase maps from DPM, single-shot images as input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules, such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1,000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational Interference Microscopy (CIM) imaging using blood smears, as they contain both erythrocytes and leukocytes, in static and dynamic conditions.
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monitoring reactivation of latent hiv by label free gradient light Interference Microscopy
bioRxiv, 2020Co-Authors: Neha Goswami, Mikhail E Kandel, Michael J Fanous, Kathrin Bohnwippert, Erin N Tevonian, Roy D Dar, Gabriel PopescuAbstract:Latent human immunodeficiency virus (HIV) reservoirs in infected individuals present the largest barrier to a cure. The first step towards overcoming this challenge is to understand the science behind latency-reactivation interplay. Fluorescence imaging of GFP-tagged HIV has been the main method for studying reactivation of latent HIV in individually infected cells. In this paper, we report insights provided by label-free, gradient light Interference Microscopy (GLIM) about the changes in measures including dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for cells with reactivated HIV as compared to those with latent disease. Results also indicate that cells with mean dry mass and diameter less than 10pg and 8μm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than 23pg and mean diameter greater than 11μm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells based on changes in cell morphology.
Hassaan Majeed - One of the best experts on this subject based on the ideXlab platform.
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quantitative histopathology of stained tissues using color spatial light Interference Microscopy cslim
Scientific Reports, 2019Co-Authors: Hassaan Majeed, Mikhail E Kandel, Krishnarao Tangella, Adib Keikhosravi, Tan H Nguyen, Andre Kajdacsyballa, Kevin W Eliceiri, Gabriel PopescuAbstract:Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light Interference Microscopy (cSLIM) as a new whole-slide imaging modality that performs interferometric imaging on stained tissue, with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide stain-independent prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.
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quantitative histopathology of stained tissues using color spatial light Interference Microscopy cslim
arXiv: Medical Physics, 2018Co-Authors: Hassaan Majeed, Mikhail E Kandel, Krishnarao Tangella, Adib Keikhosravi, Tan H Nguyen, Andre Kajdacsyballa, Kevin W Eliceiri, Gabriel PopescuAbstract:Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light Interference Microscopy (cSLIM) as a new whole slide imaging modality that performs interferometric imaging with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide not only diagnostic but also prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a simple mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.
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label free quantitative evaluation of breast tissue using spatial light Interference Microscopy slim
Scientific Reports, 2018Co-Authors: Hassaan Majeed, Mikhail E Kandel, Tan H Nguyen, Andre Kadjdacsyballa, Gabriel PopescuAbstract:Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel Microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects −34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.
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label free quantitative screening of breast tissue using spatial light Interference Microscopy slim
bioRxiv, 2017Co-Authors: Hassaan Majeed, Mikhail E Kandel, Tan H Nguyen, Andre Kadjdacsyballa, Gabriel PopescuAbstract:Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis. We present a quantitative method for label-free tissue screening using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective and potentially automatable method for rapidly flagging suspicious tissue. We demonstrated our method by imaging a tissue microarray comprising 68 different subjects - 34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. The quantitative biomarkers we extract provide a repeatable and objective basis for determining malignancy. Thus, these disease signatures can be automatically classified through machine learning packages, since our images do not vary from scan to scan or instrument to instrument, i.e., they represent intrinsic physical attributes of the sample, independent of staining quality.
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breast cancer diagnosis using spatial light Interference Microscopy
Journal of Biomedical Optics, 2015Co-Authors: Hassaan Majeed, Mikhail E Kandel, Kevin Han, Zelun Luo, Virgilia Macias, Krishnarao Tangella, Andre Balla, Gabriel PopescuAbstract:The standard practice in histopathology of breast cancers is to examine a hematoxylin and eosin (H&E) stained tissue biopsy under a microscope to diagnose whether a lesion is benign or malignant. This determination is made based on a manual, qualitative inspection, making it subject to investigator bias and resulting in low throughput. Hence, a quantitative, label-free, and high-throughput diagnosis method is highly desirable. We present here preliminary results showing the potential of quantitative phase imaging for breast cancer screening and help with differential diagnosis. We generated phase maps of unstained breast tissue biopsies using spatial light Interference Microscopy (SLIM). As a first step toward quantitative diagnosis based on SLIM, we carried out a qualitative evaluation of our label-free images. These images were shown to two pathologists who classified each case as either benign or malignant. This diagnosis was then compared against the diagnosis of the two pathologists on corresponding H&E stained tissue images and the number of agreements were counted. The agreement between SLIM and H&E based diagnosis was 88% for the first pathologist and 87% for the second. Our results demonstrate the potential and promise of SLIM for quantitative, label-free, and high-throughput diagnosis.
Tan H Nguyen - One of the best experts on this subject based on the ideXlab platform.
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quantitative histopathology of stained tissues using color spatial light Interference Microscopy cslim
Scientific Reports, 2019Co-Authors: Hassaan Majeed, Mikhail E Kandel, Krishnarao Tangella, Adib Keikhosravi, Tan H Nguyen, Andre Kajdacsyballa, Kevin W Eliceiri, Gabriel PopescuAbstract:Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light Interference Microscopy (cSLIM) as a new whole-slide imaging modality that performs interferometric imaging on stained tissue, with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide stain-independent prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.
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quantitative histopathology of stained tissues using color spatial light Interference Microscopy cslim
arXiv: Medical Physics, 2018Co-Authors: Hassaan Majeed, Mikhail E Kandel, Krishnarao Tangella, Adib Keikhosravi, Tan H Nguyen, Andre Kajdacsyballa, Kevin W Eliceiri, Gabriel PopescuAbstract:Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light Interference Microscopy (cSLIM) as a new whole slide imaging modality that performs interferometric imaging with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide not only diagnostic but also prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a simple mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.
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label free quantitative evaluation of breast tissue using spatial light Interference Microscopy slim
Scientific Reports, 2018Co-Authors: Hassaan Majeed, Mikhail E Kandel, Tan H Nguyen, Andre Kadjdacsyballa, Gabriel PopescuAbstract:Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel Microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects −34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.
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label free quantitative screening of breast tissue using spatial light Interference Microscopy slim
bioRxiv, 2017Co-Authors: Hassaan Majeed, Mikhail E Kandel, Tan H Nguyen, Andre Kadjdacsyballa, Gabriel PopescuAbstract:Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis. We present a quantitative method for label-free tissue screening using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective and potentially automatable method for rapidly flagging suspicious tissue. We demonstrated our method by imaging a tissue microarray comprising 68 different subjects - 34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. The quantitative biomarkers we extract provide a repeatable and objective basis for determining malignancy. Thus, these disease signatures can be automatically classified through machine learning packages, since our images do not vary from scan to scan or instrument to instrument, i.e., they represent intrinsic physical attributes of the sample, independent of staining quality.
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spatial light Interference Microscopy slim using twisted nematic liquid crystal modulation
Biomedical Optics Express, 2013Co-Authors: Tan H Nguyen, Gabriel PopescuAbstract:We report the use of a twisted nematic liquid-crystal spatial light modulator (TNLC-SLM) for quantitative phase imaging. The experimental setup is a new implementation of the SLIM principle, which is a phase shifting, white light method for quantitative phase imaging. The approach is based on switching between the phase and amplitude modulation modes of the SLM. Our system is able to deliver a 0.99 nm spatial and 1.33 nm temporal pathlength sensitivity while retaining the optical transverse resolution. The system is implemented as an additional module mounted to a conventional microscope, which makes the system very easy to deploy and integrate with other imaging modalities.
Zhuo Wang - One of the best experts on this subject based on the ideXlab platform.
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optical measurement of cycle dependent cell growth
Proceedings of the National Academy of Sciences of the United States of America, 2011Co-Authors: Zhuo Wang, Zhe She, Michael Ednarz, Rashid Ashi, Ido Golding, Supriya G Prasanth, Gabriel PopescuAbstract:Determining the growth patterns of single cells offers answers to some of the most elusive questions in contemporary cell biology: how cell growth is regulated and how cell size distributions are maintained. For example, a linear growth in time implies that there is no regulation required to maintain homeostasis; an exponential pattern indicates the opposite. Recently, there has been great effort to measure single cells using microelectromechanical systems technology, and several important questions have been explored. However, a unified, easy-to-use methodology to measure the growth rate of individual adherent cells of various sizes has been lacking. Here we demonstrate that a newly developed optical interferometric technique, known as spatial light Interference Microscopy, can measure the cell dry mass of many individual adherent cells in various conditions, over spatial scales from micrometers to millimeters, temporal scales ranging from seconds to days, and cell types ranging from bacteria to mammalian cells. We found evidence of exponential growth in Escherichia coli, which agrees very well with other recent reports. Perhaps most importantly, combining spatial light Interference Microscopy with fluorescence imaging provides a unique method for studying cell cycle-dependent growth. Thus, by using a fluorescent reporter for the S phase, we measured single cell growth over each phase of the cell cycle in human osteosarcoma U2OS cells and found that the G2 phase exhibits the highest growth rate, which is mass-dependent and can be approximated by an exponential.
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cell imaging beyond the diffraction limit using sparse deconvolution spatial light Interference Microscopy
Biomedical Optics Express, 2011Co-Authors: Derin S Babacan, Zhuo Wang, Gabriel PopescuAbstract:We present an imaging method, dSLIM, that combines a novel deconvolution algorithm with spatial light Interference Microscopy (SLIM), to achieve 2.3x resolution enhancement with respect to the diffraction limit. By exploiting the sparsity of the phase images, which is prominent in many biological imaging applications, and modeling of the image formation via complex fields, the very fine structures can be recovered which were blurred by the optics. With experiments on SLIM images, we demonstrate that significant improvements in spatial resolution can be obtained by the proposed approach. Moreover, the resolution improvement leads to higher accuracy in monitoring dynamic activity over time. Experiments with primary brain cells, i.e. neurons and glial cells, reveal new subdiffraction structures and motions. This new information can be used for studying vesicle transport in neurons, which may shed light on dynamic cell functioning. Finally, the method is flexible to incorporate a wide range of image models for different applications and can be utilized for all imaging modalities acquiring complex field images.
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label free intracellular transport measured by spatial light Interference Microscopy
Journal of Biomedical Optics, 2011Co-Authors: Zhuo Wang, Huafeng Ding, Larry J Millet, Martha U Gillette, Vincent Chan, Rashid Bashir, Gabriel PopescuAbstract:We show that applying the Laplace operator to a speckle-free quantitative phase image reveals an unprecedented level of detail in cell structure, without the gradient artifacts associated with differential Interference contrast Microscopy, or photobleaching and phototoxicity limitations common in fluorescence Microscopy. This method, referred to as Laplace phase Microscopy, is an efficient tool for tracking vesicles and organelles in living cells. The principle is demonstrated by tracking organelles in cardiomyocytes and vesicles in neurites of hippocampal neurons, which to our knowledge are the first label-free diffusion measurements of the organelles in such cells.
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Spatial light Interference Microscopy (SLIM)
IEEE Photonic Society 24th Annual Meeting PHO 2011, 2011Co-Authors: Zhuo Wang, Huafeng Ding, Larry J Millet, Sakulsuk Unarunotai, John A Rogers, Martha U Gillette, Gabriel PopescuAbstract:We present spatial light Interference Microscopy (SLIM) as a new optical Microscopy technique, capable of measuring nanoscale structures and dynamics in live cells via interferometry. SLIM combines two classic ideas in light imaging: Zernike's phase contrast Microscopy, which renders high contrast intensity images of transparent specimens, and Gabor's holography, where the phase information from the object is recorded. Thus, SLIM reveals the intrinsic contrast of cell structures and, in addition, renders quantitative optical path-length maps across the sample. The resulting topographic accuracy is comparable to that of atomic force Microscopy, while the acquisition speed is 1,000 times higher. We illustrate the novel insight into cell dynamics via SLIM by experiments on primary cell cultures from the rat brain. SLIM is implemented as an add-on module to an existing phase contrast microscope, which may prove instrumental in impacting the light Microscopy field at a large scale.
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topography and refractometry of nanostructures using spatial light Interference Microscopy
Optics Letters, 2010Co-Authors: Zhuo Wang, Larry J Millet, Martha U Gillette, Ik Su Chun, Xiuling Li, Gabriel PopescuAbstract:Spatial light Interference Microscopy (SLIM) is a novel method developed in our laboratory that provides quantitative phase images of transparent structures with a 0.3 nm spatial and 0.03 nm temporal accuracy owing to the white light illumination and its common path interferometric geometry. We exploit these features and demonstrate SLIM's ability to perform topography at a single atomic layer in graphene. Further, using a decoupling procedure that we developed for cylindrical structures, we extract the axially averaged refractive index of semiconductor nanotubes and a neurite of a live hippocampal neuron in culture. We believe that this study will set the basis for novel high-throughput topography and refractometry of man-made and biological nanostructures.