The current practice of surgical pathology relies on external contrast agents to reveal tissue architecture, which is then qualitatively examined by a trained pathologist. The diagnosis is based on the comparison with standardized empirical, qualitative assessments of limited objectivity. We propose an approach to pathology based on interferometric imaging of “unstained” biopsies, which provides unique capabilities for quantitative diagnosis and automation. We developed a label-free tissue scanner based on “quantitative phase imaging,” which maps out optical path length at each point in the field of view and, thus, yields images that are sensitive to the “nanoscale” tissue architecture. Unlike analysis of stained tissue, which is qualitative in nature and affected by color balance, staining strength and imaging conditions, optical path length measurements are intrinsically quantitative, i.e., images can be compared across different instruments and clinical sites. These critical features allow us to automate the diagnosis process. We paired our interferometric optical system with highly parallelized, dedicated software algorithms for data acquisition, allowing us to image at a throughput comparable to that of commercial tissue scanners while maintaining the nanoscale sensitivity to morphology. Based on the measured phase information, we implemented software tools for autofocusing during imaging, as well as image archiving and data access. To illustrate the potential of our technology for large volume pathology screening, we established an “intrinsic marker” for colorectal disease that detects tissue with dysplasia or colorectal cancer and flags specific areas for further examination, potentially improving the efficiency of existing pathology workflows.
Optimal growth as well as branching of axons and dendrites is critical for the nervous system function. Neuritic length, arborization, and growth rate determine the innervation properties of neurons and define each cell’s computational capability. Thus, to investigate the nervous system function, we need to develop methods and instrumentation techniques capable of quantifying various aspects of neural network formation: neuron process extension, retraction, stability, and branching. During the last three decades, fluorescence microscopy has yielded enormous advances in our understanding of neurobiology. While fluorescent markers provide valuable specificity to imaging, photobleaching, and photoxicity often limit the duration of the investigation. Here, we used spatial light interference microscopy (SLIM) to measure quantitatively neurite outgrowth as a function of cell confluence. Because it is label-free and nondestructive, SLIM allows for long-term investigation over many hours. We found that neurons exhibit a higher growth rate of neurite length in low-confluence versus medium- and high-confluence conditions. We believe this methodology will aid investigators in performing unbiased, nondestructive analysis of morphometric neuronal parameters.
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.
We provide an experimental demonstration of Spatial Light Interference Microscopy (SLIM) as a tool for measuring the motion of 25 nm tubulin structures without the use of florescence labels. Compared to intensity imaging methods such as phase contrast or DIC, our imaging technique relies on the ratios of images associated with optically introduced phase shifts, thus implicitly removing background illumination. To demonstrate our new found capabilities, we characterize kinesin-based motility continuously over periods of time where fluorescence would typically photobleach. We exploit this new method to compare the motility of microtubules at low ATP concentrations, with and without the tagging proteins formerly required to perform these studies. Our preliminary results show that the tags have a non-negligible effect on the microtubule motility, slowing the process down by more than 10%.
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.
The multi-shot approach in SLIM requires reliable, synchronous, and parallel operation of three independent hardware devices – not meeting these challenges results in degraded phase and slow acquisition speeds, narrowing applications to holistic statements about complex phenomena. The relative youth of quantitative imaging and the lack of ready-made commercial hardware and tools further compounds the problem as Higher level programming languages result in inflexible, experiment specific instruments limited by ill-fitting computational modules, resulting in a palpable chasm between promised and realized hardware performance. Furthermore, general unfamiliarity with intricacies such as background calibration, objective lens attenuation, along with spatial light modular alignment, makes successful measurements difficult for the inattentive or uninitiated. This poses an immediate challenge for moving our techniques beyond the lab to biologically oriented collaborators and clinical practitioners.
To meet these challenges, we present our new Quantitative Phase Imaging pipeline, with improved instrument performance, friendly user interface and robust data processing features, enabling us to acquire and catalog clinical datasets hundreds of gigapixels in size.
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.
We used a new quantitative high spatiotemporal resolution phase imaging tool to explore the nuclear structure and dynamics of individual cells. We used a novel analysis tool to quantify the diffusion outside and inside the nucleus of live cells. We also obtained information about the nuclear spatio temporal mass density in metastatic cells. The results indicate that in the cytoplasm, the intracellular transport is mainly active (direct, deterministic), while inside the nucleus it is both active and passive (diffusive, random). We calculated the standard deviation of velocities in active transport and the diffusion coefficient for passive transport.
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.