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Dark-field microscopy is a standard imaging technique widely employed in biology that provides high image contrast for a broad range of unstained specimens1. Unlike bright-field microscopy, it accentuates high spatial frequencies and can therefore be used to emphasize and resolve small features. However, the use of dark-field microscopy for reliable analysis of blood cells, bacteria, algae and other marine organisms often requires specialized, bulky microscope systems, as well as expensive additional components, such as dark-field-compatible objectives or condensers2,3. Here, we propose to simplify and downsize dark-field microscopy equipment by generating the high-angle illumination cone required for dark-field microscopy directly within the sample substrate. We introduce a luminescent photonic substrate with a controlled angular emission profile and demonstrate its ability to generate high-contrast dark-field images of micrometre-sized living organisms using standard optical microscopy equipment. This new type of substrate forms the basis for miniaturized lab-on-chip dark-field imaging devices that are compatible with simple and compact light microscopes.A luminescent photonic substrate with a controlled angular emission profile is introduced and its ability to generate high-contrast dark-field images of micrometre-sized living organisms is demonstrated using standard optical microscopy equipment.

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.

Optical monitoring of blood glucose levels for non-invasive diagnosis is a growing area of research. Recent efforts in this direction have been inclined towards reducing the requirement of calibration framework. Here, we are presenting a systematic investigation on the influence of variation in the ratio of calibration and validation points on the prospective predictive accuracy of spectral models. A fiber-optic probe coupled Raman system has been employed for transcutaneous measurements. Limit of agreement analysis between serum and partial least square regression predicted spectroscopic glucose values has been performed for accurate comparison. Findings are suggestive of strong predictive accuracy of spectroscopic models without requiring substantive calibration measurements.

Cells inside a 3D matrix (such as tissue extracellular matrix or biomaterials) sense their insoluble environment through specific binding interactions between their adhesion receptors and ligands present on the matrix surface. Despite the critical role of the insoluble matrix in cell regulation, there exist no widely-applicable methods for quantifying the chemical stimuli provided by a matrix to cells. Here, we describe a general-purpose technique for quantifying in situ the density of ligands for specific cell adhesion receptors of interest on the surface of a 3D matrix. This paper improves significantly the accuracy of the procedure introduced in a previous publication by detailed marker characterization, optimized staining, and improved data interpretation. The optimized methodology is utilized to quantify the ligands of integrins α 1 β 1 , α 2 β 1 on two kinds of matched porous collagen scaffolds, which are shown to possess significantly different ligand density, and significantly different ability to induce peripheral nerve regeneration in vivo . Data support the hypothesis that cell adhesion regulates contractile cell phenotypes, recently shown to be inversely related to organ regeneration. The technique provides a standardized way to quantify the surface chemistry of 3D matrices, and a means for introducing matrix effects in quantitative biological models.

Homologous recombination (HR) events are key drivers of cancer-promoting mutations, and the ability to visualize these events in situ provides important information regarding mutant cell type, location, and clonal expansion. We have previously created the R os a 26 D irect R epeat (RaDR) mouse model wherein HR at an integrated substrate gives rise to a fluorescent cell. To fully leverage this in situ approach, we need better ways to quantify rare fluorescent cells deep within tissues. Here, we present a robust, automated event quantification algorithm that uses image intensity and gradient features to detect fluorescent cells in deep tissue specimens. To analyze the performance of our algorithm, we simulate fluorescence behavior in tissue using Monte Carlo methods. Importantly, this approach reduces the potential for bias in manual counting and enables quantification of samples with highly dense HR events. Using this approach, we measured the relative frequency of HR within a chromosome and between chromosomes and found that HR within a chromosome is more frequent, which is consistent with the close proximity of sister chromatids. Our approach is both objective and highly rapid, providing a powerful tool, not only to researchers interested in HR, but also to many other researchers who are similarly using fluorescence as a marker for understanding mammalian biology in tissues.

We apply the technique of second-harmonic generation (SHG) microscopy to obtain large area submicron resolution image of Type I collagen from rat tail tendon as it is heated from 40°C to 70°C for 0–180 min. The change in the collagen structure as reflected in its SHG image is observed at length scales from submicron to hundreds of microns. We observed that heating the tendon below the temperature of 54°C does not produce any change in the averaged SHG intensity. At the heating temperature of 54°C and above, we find that increasing the heating temperature and time leads to decreasing SHG intensity. As the tendon is heated above 54°C, the regions where the SHG signal vanish and form a tiger-tail like pattern. In addition, a decrease in the SHG signal occurs uniformly throughout the tendon. By comparing the relative SHG intensities in small and large areas, we found that the denaturation process responsible for forming the tiger-tail like pattern occurs at a higher rate than the global denaturation process occurring throughout the tendon. We also measured the fibril spacing and found that it remains constant at 1.61 ± 0.04 micron for all heating temperature and times. The constant fibril density shows that the global denaturation process occurs at a length scale smaller than the size of the fibril. Our results show that second-harmonic generation microscopy is effective in monitoring the thermal damage to collagen and has potential applications in biomedicine.

Fluorescence correlation spectroscopy (FCS) is generally used to obtain information about the number of fluorescent particles in a small volume and the diffusion coefficient from the autocorrelation function of the fluorescence signal. Here we demonstrate that photon counting histogram (PCH) analysis constitutes a novel tool for extracting quantities from fluorescence fluctuation data, i.e., the measured photon counts per molecule and the average number of molecules within the observation volume. The photon counting histogram of fluorescence fluctuation experiments, in which few molecules are present in the excitation volume, exhibits a super-Poissonian behavior. The additional broadening of the PCH compared to a Poisson distribution is due to fluorescence intensity fluctuations. For diffusing particles these intensity fluctuations are caused by an inhomogeneous excitation profile and the fluctuations in the number of particles in the observation volume N¯. The quantitative relationship between the detected photon counts and the fluorescence intensity reaching the detector is given by Mandel's formula. Based on this equation and considering the fluorescence intensity distribution in the two-photon excitation volume, a theoretical expression for the PCH as a function of the number of molecules in the excitation volume is derived. For a single molecular species two parameters are sufficient to characterize the histogram completely, namely the average number of molecules within the observation volume and the detected photon counts per molecule per sampling time ϵ. The PCH for multiple molecular species, on the other hand, is generated by successively convoluting the photon counting distribution of each species with the others. The influence of the excitation profile upon the photon counting statistics for two relevant point spread functions (PSFs), the three-dimensional Gaussian PSF conventionally employed in confocal detection and the square of the Gaussian-Lorentzian PSF for two photon excitation, is explicitly treated. Measured photon counting distributions obtained with a two-photon excitation source agree, within experimental error with the theoretical PCHs calculated for the square of a Gaussian-Lorentzian beam profile. We demonstrate and discuss the influence of the average number of particles within the observation volume and the detected photon counts per molecule per sampling interval upon the super-Poissonian character of the photon counting distribution.

The development of high resolution, high speed imaging techniques allows the study of dynamical processes in biological systems. Lateral resolution improvement of up to a factor of 2 has been achieved using structured illumination. In a total internal reflection fluorescence microscope, an evanescence excitation field is formed as light is total internally reflected at an interface between a high and a low index medium. The <100 nm penetration depth of evanescence field ensures a thin excitation region resulting in low background fluorescence. We present even higher resolution wide-field biological imaging by use of standing wave total internal reflection fluorescence (SW-TIRF). Evanescent standing wave (SW) illumination is used to generate a sinusoidal high spatial frequency fringe pattern on specimen for lateral resolution enhancement. To prevent thermal drift of the SW, novel detection and estimation of the SW phase with real-time feedback control is devised for the stabilization and control of the fringe phase. SW-TIRF is a wide-field superresolution technique with resolution better than a fifth of emission wavelength or ∼100 nm lateral resolution. We demonstrate the performance of the SW-TIRF microscopy using one- and two-directional SW illumination with a biological sample of cellular actin cytoskeleton of mouse fibroblast cells as well as single semiconductor nanocrystal molecules. The results confirm the superior resolution of SW-TIRF in addition to the merit of a high signal/background ratio from TIRF microscopy.

Two-photon microscopy is used to image neuronal activity, but has severe limitations for studying deeper cortical layers. Here, we developed a custom three-photon microscope optimized to image a vertical column of the cerebral cortex > 1 mm in depth in awake mice with low (<20 mW) average laser power. Our measurements of physiological responses and tissue-damage thresholds define pulse parameters and safety limits for damage-free three-photon imaging. We image functional visual responses of neurons expressing GCaMP6s across all layers of the primary visual cortex (V1) and in the subplate. These recordings reveal diverse visual selectivity in deep layers: layer 5 neurons are more broadly tuned to visual stimuli, whereas mean orientation selectivity of layer 6 neurons is slightly sharper, compared to neurons in other layers. Subplate neurons, located in the white matter below cortical layer 6 and characterized here for the first time, show low visual responsivity and broad orientation selectivity. Two-photon microscopy is a powerful tool for studying neuronal activity but cannot easily image deeper cortical layers. Here, the authors design a custom microscope for three-photon microscopy and use it to reveal response properties of layer 5, 6, and subplate visual cortical neurons.