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"So, Peter"
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Functional imaging of visual cortical layers and subplate in awake mice with optimized three-photon 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.
Journal Article
Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA
Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.
Transcriptional profiles of single living cells are generated from Raman imaging data.
Journal Article
Luminescent surfaces with tailored angular emission for compact dark-field imaging devices
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.
Journal Article
In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui
During metamorphosis, the wings of a butterfly sprout hundreds of thousands of scales with intricate microstructures and nanostructures that determine the wings’ optical appearance, wetting characteristics, thermodynamic properties, and aerodynamic behavior. Although the functional characteristics of scales are well known and prove desirable in various applications, the dynamic processes and temporal coordination required to sculpt the scales’ many structural features remain poorly understood. Current knowledge of scale growth is primarily gained from ex vivo studies of fixed scale cells at discrete time points; to fully understand scale formation, it is critical to characterize the time-dependent morphological changes throughout their development. Here, we report the continuous, in vivo, label-free imaging of growing scale cells of Vanessa cardui using speckle-correlation reflection phase microscopy. By capturing time-resolved volumetric tissue data together with nanoscale surface height information, we establish a morphological timeline of wing scale formation and gain quantitative insights into the underlying processes involved in scale cell patterning and growth. We identify early differences in the patterning of cover and ground scales on the young wing and quantify geometrical parameters of growing scale features, which suggest that surface growth is critical to structure formation. Our quantitative, time-resolved in vivo imaging of butterfly scale development provides the foundation for decoding the processes and biomechanical principles involved in the formation of functional structures in biological materials.
Journal Article
Absorption by water increases fluorescence image contrast of biological tissue in the shortwave infrared
Recent technology developments have expanded the wavelength window for biological fluorescence imaging into the shortwave infrared. We show here a mechanistic understanding of how drastic changes in fluorescence imaging contrast can arise from slight changes of imaging wavelength in the shortwave infrared. We demonstrate, in 3D tissue phantoms and in vivo in mice, that light absorption by water within biological tissue increases image contrast due to attenuation of background and highly scattered light. Wavelengths of strong tissue absorption have conventionally been avoided in fluorescence imaging to maximize photon penetration depth and photon collection, yet we demonstrate that imaging at the peak absorbance of water (near 1,450 nm) results in the highest image contrast in the shortwave infrared. Furthermore, we show, through microscopy of highly labeled ex vivo biological tissue, that the contrast improvement from water absorption enables resolution of deeper structures, resulting in a higher imaging penetration depth. We then illustrate these findings in a theoretical model. Our results suggest that the wavelength-dependent absorptivity of water is the dominant optical property contributing to image contrast, and is therefore crucial for determining the optimal imaging window in the infrared.
Journal Article
Label-free three-photon imaging of intact human cerebral organoids for tracking early events in brain development and deficits in Rett syndrome
Human cerebral organoids are unique in their development of progenitor-rich zones akin to ventricular zones from which neuronal progenitors differentiate and migrate radially. Analyses of cerebral organoids thus far have been performed in sectioned tissue or in superficial layers due to their high scattering properties. Here, we demonstrate label-free three-photon imaging of whole, uncleared intact organoids (~2 mm depth) to assess early events of early human brain development. Optimizing a custom-made three-photon microscope to image intact cerebral organoids generated from Rett Syndrome patients, we show defects in the ventricular zone volumetric structure of mutant organoids compared to isogenic control organoids. Long-term imaging live organoids reveals that shorter migration distances and slower migration speeds of mutant radially migrating neurons are associated with more tortuous trajectories. Our label-free imaging system constitutes a particularly useful platform for tracking normal and abnormal development in individual organoids, as well as for screening therapeutic molecules via intact organoid imaging.
Journal Article
Tumor cell nuclei soften during transendothelial migration
During cancer metastasis, tumor cells undergo significant deformation in order to traverse through endothelial cell junctions in the walls of blood vessels. As cells pass through narrow gaps, smaller than the nuclear diameter, the spatial configuration of chromatin must change along with the distribution of nuclear enzymes. Nuclear stiffness is an important determinant of the ability of cells to undergo transendothelial migration, yet no studies have been conducted to assess whether tumor cell cytoskeletal or nuclear stiffness changes during this critical process in order to facilitate passage. To address this question, we employed two non-contact methods, Brillouin confocal microscopy (BCM) and confocal reflectance quantitative phase microscopy (QPM), to track the changes in mechanical properties of live, transmigrating tumor cells in an in vitro collagen gel platform. Using these two imaging modalities to study transmigrating MDA-MB-231, A549, and A375 cells, we found that both the cells and their nuclei soften upon extravasation and that the nuclear membranes remain soft for at least 24 h. These new data suggest that tumor cells adjust their mechanical properties in order to facilitate extravasation.
Journal Article
Deep learning enables automated scoring of liver fibrosis stages
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.
Journal Article