Summary Anatomical information at the cellular level is important in many fields, including organ systems development, computational biology and informatics. Creating data sets at resolutions that provide enough detail to reconstruct cellular structures across tissue volumes from 1 to 100 mm3 has proven to be difficult and time‐consuming. In this paper, we describe a new method for staining and imaging large volumes of tissue at sub‐micron resolutions. Serial sections are cut using an automated ultra‐microtome, whereas concurrently each section is imaged through a light microscope with a high‐speed line‐scan camera. This technique, knife‐edge scanning microscopy, allows us to view and record large volumes of tissue in a relatively small amount of time (approximately 7 mm2 s−1). The resolution and scanning speed of knife‐edge scanning microscopy provides a new method for imaging tissue at sufficient resolution to reconstruct maps of cellular distribution and morphology. We show that these techniques preserve the alignment of serial sections accurately enough to allow for reconstruction of neuronal processes and microvasculature. Expanding these techniques to other tissues opens up the possibility of creating fully reconstructed cellular maps of entire organs.
The observation of low-energy edge photoluminescence and its beneficial effect on the solar cell efficiency of Ruddlesden−Popper perovskites has unleashed an intensive research effort to reveal its origin. This effort, however, has been met with more challenges as the underlying material structure has still not been identified; new modelings and observations also do not seem to converge. Using twodimensional (2D) (BA) 2 (MA) 2 Pb 3 Br 10 as an example, we show that threedimensional (3D) MAPbBr 3 is formed due to the loss of BA on the edge. This self-formed MAPbBr 3 can explain the reported edge emission under various conditions, while the reported intriguing optoelectronic properties such as fast exciton trapping from the interior 2D perovskite, rapid exciton dissociation, and long carrier lifetime can be understood via the self-formed 2D/3D lateral perovskite heterostructure. The 3D perovskite is identified by submicron infrared spectroscopy, the emergence of X-ray diffraction (XRD) signature from freezer-milled nanometer-sized 2D perovskite, and its photoluminescence response to external hydrostatic pressure. The revelation of this edge emission mystery and the identification of a self-formed 2D/3D heterostructure provide a new approach to engineering 2D perovskites for high-performance optoelectronic devices.
An infrared spectrum recorded from a microscopic sample depends on spectral properties of the constituent material as well as on morphology. Many samples or domains within heterogeneous materials can be idealized as spheres, in which both scattering and absorption from the three-dimensional shape affect the recorded spectrum. Spectra recorded from such objects may be altered to such an extent that they bear little resemblance to spectra recorded from the bulk material; there are no methods, however, to reconcile the two from first principles. Here we provide the mathematical description of the optical physics underlying light-spherical sample interaction within an instrument. We use the developed analytical expressions to predict recorded data from spheres using Fourier transform infrared (FT-IR) spectroscopic imaging. Recorded spectra are shown to depend strongly on the size of the sphere as well as the optical arrangement of the instrument. Next, we present theory and experiments demonstrating the recovery of the complex refractive index of the material using data recorded from a sphere. The effects of the sample morphology on the measured spectra can be removed, and using the imaginary part of the index, the shape-independent IR absorption spectrum of the material is recovered.
Dyes such as hematoxylin and eosin (H&E) and immunohistochemical stains have been increasingly used to visualize tissue composition in research and clinical practice. We present an alternative approach to obtain the same information using stain-free chemical imaging. Relying on Fourier transform infrared (FT-IR) spectroscopic imaging and computation, stainless computed histopathology can enable a rapid, digital, quantitative and non-perturbing visualization of morphology and multiple molecular epitopes simultaneously in a variety of research and clinical pathology applications.
Current methods for cancer detection rely on tissue biopsy, chemical labeling/staining, and examination of the tissue by a pathologist. Though these methods continue to remain the gold standard, they are non-quantitative and susceptible to human error. Fourier transform infrared (FTIR) spectroscopic imaging has shown potential as a quantitative alternative to traditional histology. However, identification of histological components requires reliable classification based on molecular spectra, which are susceptible to artifacts introduced by noise and scattering. Several tissue types, particularly in heterogeneous tissue regions, tend to confound traditional classification methods. Convolutional neural networks (CNNs) are the current state-of-the-art in image classification, providing the ability to learn spatial characteristics of images. In this paper, we demonstrate that CNNs with architectures designed to process both spectral and spatial information can significantly improve classifier performance over per-pixel spectral classification. We report classification results after applying CNNs to data from tissue microarrays (TMAs) to identify six major cellular and acellular constituents of tissue, namely adipocytes, blood, collagen, epithelium, necrosis, and myofibroblasts. Experimental results show that the use of spatial information in addition to the spectral information brings significant improvements in the classifier performance and allows classification of cellular subtypes, such as adipocytes, that exhibit minimal chemical information but have distinct spatial characteristics. This work demonstrates the application and efficiency of deep learning algorithms in improving the diagnostic techniques in clinical and research activities related to cancer.
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