Histological staining of tissue samples is one of the most helpful tools in diagnosing and prognosing various cancers. However, in order to prepare the slide for a histopathologist to examine, the tissue must first undergo a series of time-consuming processes, such as a staining technique to visually differentiate features in the sample.
In this study, we use a label-free method to generate a virtually-stained microscopic image using a single spatial light interference microscopy (SLIM) image of an unlabeled tissue sample, therefore eliminating the need for standard histochemical administration.
This novel approach will render histopathological practices faster and more cost-effective, while providing medically relevant dry mass information associated with SLIM images.
The intrinsic markers of nanoscale morphological alteration in fixed tissue biopsy referred to as disorder strength and local correlation length, which can be easily and time-efficiently obtained from quantitative phase images, are introduced. After presenting how to extract these markers from quantitative phase images obtained by highly sensitive quantitative phase imaging system, spatial light interference microscopy (SLIM), we demonstrate the effectiveness of these markers for diagnosis of benign and malignant breast tissues.
We propose an intrinsic cancer marker in fixed tissue biopsy slides, which is based on the local spatial autocorrelation length obtained from quantitative phase images. The spatial autocorrelation length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation length maps are calculated by computing two-dimensional Fourier transforms over image subregions—requiring long computational times. We propose a more time-efficient method of computing the correlation map and demonstrate its value for diagnosis of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy.
The standard method for cancer diagnosis is the microscopic investigation of tissue biopsies. Because the tissues do not significantly absorb and scatter light, traditionally, the observation is performed using bright-field microscopy after staining. Although this approach has been widely adopted all over the world for 100 years, it generally takes a long preparation time and sometimes the early carcinogenesis is missed due to a variation in a quality of a staining. Quantitative phase imaging (QPI) can access objective information on thickness and refractive index changes from an unstained tissue slice, which cannot be observed by conventional microscopes. This can be an attractive advantage in the field of a medical diagnosis, especially since QPI can access the tissue architecture information with nanoscale sensitivity. In this paper, we used quantitative phase imaging to measure the tissue disorder strength, which is known as one of the effective markers of early carcinogenesis. We retrieved the disorder parameter from the local refractive index fluctuation map obtained by spatial light interference microscopy (SLIM). We show that SLIM imaging combined with the disorder analysis is a valuable approach for screening of benign and malignant breast tissue biopsies.
Quantitative phase imaging (QPI) can access quantitative information on thickness and/or refractive index changes of weakly absorbing and scattering objects, which normally require staining prior to observation. The quantitative phase image itself yields significant information for a medical diagnosis, particularly in cancer biopsies. Previously, several parameters such as a local standard deviation of refractive index have been utilized as a marker of diseases. We focus on the local spatial autocorrelation length, which is calculated at each point in the field of view. The local spatial autocorrelation length is defined as the standard deviation of the local spatial autocorrelation function and reveals the local and directional disorder information of tissues. However, generally, a direct calculation of the local spatial autocorrelation length take an immense amount of time. In this paper, we propose a high-throughput calculation procedure of a local spatial autocorrelation length, by exploiting frequency-domain calculations. After deriving a simple equation to calculate the local spatial autocorrelation length map in a short time, we perform label-free screening of benign and malignant breast tissue biopsies using this parameter as a marker.
Histopathology of breast tissue typically involves labelling of a tissue section with the Hematoxylin and Eosin (H&E) counter stain. The contrast generated by the stain allows pathologists to extract both diagnostic and prognostic information (such as malignancy, grade and stage) during microscopic examination. Despite being the most frequent assessment method in use today, this procedure provides limited information and is subject to observer bias due to its qualitative nature. While commercial tissue slide scanners promise to improve the throughput of this method in the coming years, quantitative evaluation of tissue remains a challenge. In this work, we propose a method for simultaneously extracting color bright field and phase images of stained breast tissue biopsies using Spatial Light Interference Microscopy (SLIM) coupled with an RGB camera. The amplitude information allows standard qualitative histopathology while the quantitative phase information can be used, after normalizing for the staining, to extract physical markers characterizing patient health. We demonstrate this by imaging an H&E stained tissue microarray and showing that the normalized phase values can be exploited to classify benign and malignant tissue. Furthermore, we demonstrate that these images allow quantitation of tumor-adjacent collagen structure - an important prognostic marker for breast cancer. Extraction of these biomarkers requires measurement of the tissue optical path-length map which is not available in standard tissue evaluation. Our method, therefore, expands on the current diagnostic pipeline by complementing standard histopathology with quantitative tissue biomarkers, all obtainable in a single scan.
Traditionally the measurement of electrical activity in neurons has been carried out using microelectrode arrays that require the conducting elements to be in contact with the neuronal network. This method, also referred to as “electrophysiology”, while being excellent in terms of temporal resolution is limited in spatial resolution and is invasive. An optical microscopy method for measuring electrical activity is thus highly desired. Common-path quantitative phase imaging (QPI) systems are good candidates for such investigations as they provide high sensitivity (on the order of nanometers) to the plasma membrane fluctuations that can be linked to electrical activity in a neuronal circuit. In this work we measured electrical activity in a culture of rat cortical neurons using MISS microscopy, a high-speed common-path QPI technique having an axial resolution of around 1 nm in optical path-length, which we introduced at PW BIOS 2016. Specifically, we measured the vesicular cycling (endocytosis and exocytosis) occurring at axon terminals of the neurons due to electrical activity caused by adding a high K+ solution to the cell culture. The axon terminals were localized using a micro-fluidic device that separated them from the rest of the culture. Stacks of images of these terminals were acquired at 826 fps both before and after K+ excitation and the temporal standard deviation maps for the two cases were compared to measure the membrane fluctuations. Concurrently, the existence of vesicular cycling was confirmed through fluorescent tagging and imaging of the vesicles at and around the axon terminals.
Breast cancer is a major public health problem worldwide, being the most common type of cancer among women according to the World Health Organization (WHO). The WHO has further stressed the importance of an early determination of the disease course through prognostic markers. Recent studies have shown that the alignment of collagen fibers in tumor adjacent stroma correlate with poorer health outcomes in patients. Such studies have typically been carried out using Second-Harmonic Generation (SHG) microscopy. SHG images are very useful for quantifying collagen fiber orientation due their specificity to non-centrosymmetric structures in tissue, leading to high contrast in collagen rich areas. However, the imaging throughput in SHG microscopy is limited by its point scanning geometry. In this work, we show that SLIM, a wide-field high-throughput QPI technique, can be used to obtain the same information on collagen fiber orientation as is obtainable through SHG microscopy. We imaged a tissue microarray containing both benign and malignant cores using both SHG microscopy and SLIM. The cellular (non-collagenous) structures in the SLIM images were next segmented out using an algorithm developed in-house. Using the previously published Fourier Transform Second Harmonic Generation (FT-SHG) tool, the fiber orientations in SHG and segmented SLIM images were then quantified. The resulting histograms of fiber orientation angles showed that both SHG and SLIM generate similar measurements of collagen fiber orientation. The SLIM modality, however, can generate these results at much higher throughput due to its wide-field, whole-slide scanning capabilities.
Tumor progression in breast cancer is significantly influenced by its interaction with the surrounding stromal tissue. Specifically, the composition, orientation, and alignment of collagen fibers in tumor-adjacent stroma affect tumor growth and metastasis. Most of the work done on measuring this prognostic marker has involved imaging of collagen fibers using second-harmonic generation microscopy (SHGM), which provides label-free specificity. Here, we show that spatial light interference microscopy (SLIM), a label-free quantitative phase imaging technique, is able to provide information on collagen-fiber orientation that is comparable to that provided by SHGM. Due to its wide-field geometry, the throughput of the SLIM system is much higher than that of SHGM and, because of the linear imaging, the equipment is simpler and significantly less expensive. Our results indicate that SLIM images can be used to extract important prognostic information from collagen fibers in breast tissue, potentially providing a convenient high throughput clinical tool for assessing patient prognosis.
Traction force microscopy is the most widely used technique for studying the forces exerted by cells on deformable substrates. However, the method is computationally intense and cells have to be detached from the substrate prior to measuring the displacement map. We have developed a new method, referred to as Hilbert phase dynamometry (HPD), which yields real-time force fields and, simultaneously, cell dry mass and growth information. HPD operates by imaging cells on a deformable substrate that is patterned with a grid of fluorescent proteins. A Hilbert transform is used to extract the phase map associated with the grid deformation, which provides the displacement field. By combining this information with substrate stiffness, an elasticity model was developed to measure forces exerted by cells with high spatial resolution. In our study, we prepared 10kPa gels and them with a 2-D grid of FITC-conjugated fibrinogen/fibronectin mixture, an extracellular matrix protein to which cells adhere. We cultured undifferentiated mesenchymal stem cells (MSC), and MSCs that were in the process of undergoing adipogenesis and osteogenesis. The cells were measured over the course of 24 hours using Spatial Light Interference Microscopy (SLIM) and wide-field epi-fluorescence microscopy allowing us to simultaneously measure cell growth and the forces exerted by the cells on the substrate.
Diffraction Phase Microscopy (DPM) is a common-path, single shot QPI technique that has found applications in studies of red blood cell morphology and dynamics, cell growth measurement, as well as in Fourier Transform Light Scattering. In DPM, the phase is retrieved by interfering two orders of diffraction from a grating placed at the image plane. The reference field has been, in the past, generated by low pass filtering the zero order via a pinhole placed in the Fourier plane. For achieving the desired spatial coherence, the pinhole is often only 5-10 µm in diameter, making the system difficult to align every time an imaging study is performed. In this work, we eliminated the pinhole from the DPM optical path and generated instead the reference field by magnifying strongly the zero order. We show that a gradient-index (GRIN) lens (effective focal length of 300 µm) can be used to magnify the Fourier transform of the zero order to the point where the DC component fills the camera sensor. We show that the resulting Magnified Object Spectrum Interference Microscopy (MOSIM) system can successfully reconstruct quantitative phase images, without the need for tedious alignment. Because it conserves the common path geometry, MOSIM is characterized by 1.1 nm spatiotemporal pathlength noise. Since it is single shot, we demonstrated 400 frames/s acquisition. We anticipate that this new method can potentially lead to a more robust and less vibration sensitive QPI instrument for carrying out biological studies at various spatiotemporal scales.
The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel’s label and the histogram of these textons in that pixel’s neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel’s neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.
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.
Spatiotemporal patterns of intracellular transport are very difficult to quantify and, consequently, continue to be insufficiently understood. While it is well documented that mass trafficking inside living cells consists of both random and deterministic motions, quantitative data over broad spatiotemporal scales are lacking. We studied the intracellular transport in live cells using spatial light interference microscopy, a high spatiotemporal resolution quantitative phase imaging tool. The results indicate that in the cytoplasm, the intracellular transport is mainly active (directed, deterministic), while inside the nucleus it is both active and passive (diffusive, random). Furthermore, we studied the behavior of the two-dimensional mass density over 30 h in HeLa cells and focused on the active component. We determined the standard deviation of the velocity distribution at the point of cell division for each cell and compared the standard deviation velocity inside the cytoplasm and the nucleus. We found that the velocity distribution in the cytoplasm is consistently broader than in the nucleus, suggesting mechanisms for faster transport in the cytosol versus the nucleus. Future studies will focus on improving phase measurements by applying a fluorescent tag to understand how particular proteins are transported inside the cell.
We provide a quantitative model for image formation in common-path QPI systems under partially coherent illumination. Our model is capable of explaining the phase reduction phenomenon and halo effect in phase measurements. We further show how to fix these phenomena with a novel iterative post-processing algorithm. Halo-free and correct phase images of nanopillars and live cells are used to demonstrate the validity of our method.
The standard practice in the histopathology of breast cancers is to examine a hematoxylin and eosin (H&E) stained tissue biopsy under a microscope. The pathologist looks at certain morphological features, visible under the stain, to diagnose whether a tumor is benign or malignant. This determination is made based on qualitative inspection making it subject to investigator bias. Furthermore, since this method requires a microscopic examination by the pathologist it suffers from low throughput. A quantitative, label-free and high throughput method for detection of these morphological features from images of tissue biopsies is, hence, highly desirable as it would assist the pathologist in making a quicker and more accurate diagnosis of cancers. We present here preliminary results showing the potential of using quantitative phase imaging for breast cancer screening and help with differential diagnosis. We generated optical path length maps of unstained breast tissue biopsies using Spatial Light Interference Microscopy (SLIM). As a first step towards diagnosis based on quantitative phase imaging, we carried out a qualitative evaluation of the imaging resolution and contrast of our label-free phase images. These images were shown to two pathologists who marked the tumors present in tissue as either benign or malignant. This diagnosis was then compared against the diagnosis of the two pathologists on H&E stained tissue images and the number of agreements were counted. In our experiment, the agreement between SLIM and H&E based diagnosis was measured to be 88%. Our preliminary results demonstrate the potential and promise of SLIM for a push in the future towards quantitative, label-free and high throughput diagnosis.
While automated blood cell counters have made great progress in detecting abnormalities in blood, the lack of specificity for a particular disease, limited information on single cell morphology and intrinsic uncertainly due to high throughput in these instruments often necessitates detailed inspection in the form of a peripheral blood smear. Such tests are relatively time consuming and frequently rely on medical professionals tally counting specific cell types. These assays rely on the contrast generated by chemical stains, with the signal intensity strongly related to staining and preparation techniques, frustrating machine learning algorithms that require consistent quantities to denote the features in question. Instead we opt to use quantitative phase imaging, understanding that the resulting image is entirely due to the structure (intrinsic contrast) rather than the complex interplay of stain and sample. We present here our first steps to automate peripheral blood smear scanning, in particular a method to generate the quantitative phase image of an entire blood smear at high throughput using white light diffraction phase microscopy (wDPM), a single shot and common path interferometric imaging technique.
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