Permittivity tensor imaging: modular label-free imaging of 3D dry mass and 3D orientation at high resolution.

The dry mass and the orientation of biomolecules can be imaged without a label by measuring their permittivity tensor (PT), which describes how biomolecules affect the phase and polarization of light. Three-dimensional (3D) imaging of PT has been challenging. We present a label-free computational microscopy technique, PT imaging (PTI), for the 3D measurement of PT. PTI encodes the invisible PT into images using oblique illumination, polarization-sensitive detection and volumetric sampling. PT is decoded from the data with a vectorial imaging model and a multi-channel inverse algorithm, assuming uniaxial symmetry in each voxel. We demonstrate high-resolution imaging of PT of isotropic beads, anisotropic glass targets, mouse brain tissue, infected cells and histology slides. PTI outperforms previous label-free imaging techniques such as vector tomography, ptychography and light-field imaging in resolving the 3D orientation and symmetry of organelles, cells and tissue. We provide open-source software and modular hardware to enable the adoption of the method.

Visualizing Sphingosine-1-Phosphate Receptor 1(S1P1) Signaling During Central Nervous System De- and Remyelination.

Multiple sclerosis (MS) is an inflammatory-demyelinating disease of the central nervous system (CNS) mediated by aberrant auto-reactive immune responses. The current immune-modulatory therapies are unable to protect and repair immune-mediated neural tissue damage. One of the therapeutic targets in MS is the sphingosine-1-phosphate (S1P) pathway which signals via sphingosine-1-phosphate receptors 1-5 (S1P1-5). S1P receptors are expressed predominantly on immune and CNS cells. Considering the potential neuroprotective properties of S1P signaling, we utilized S1P1-GFP (Green fluorescent protein) reporter mice in the cuprizone-induced demyelination model to investigate in vivo S1P - S1P1 signaling in the CNS. We observed S1P1 signaling in a subset of neural stem cells in the subventricular zone (SVZ) during demyelination. During remyelination, S1P1 signaling is expressed in oligodendrocyte progenitor cells in the SVZ and mature oligodendrocytes in the medial corpus callosum (MCC). In the cuprizone model, we did not observe S1P1 signaling in neurons and astrocytes. We also observed ß-arrestin-dependent S1P1 signaling in lymphocytes during demyelination and CNS inflammation. Our findings reveal ß-arrestin-dependent S1P1 signaling in oligodendrocyte lineage cells implying a role of S1P1 signaling in remyelination.

Assessing tumor angiogenesis using dynamic contrast-enhanced integrated magnetic resonance-positron emission tomography in patients with non-small-cell lung cancer.

BACKGROUND: Angiogenesis assessment is important for personalized therapeutic intervention in patients with non-small-cell lung cancer (NSCLC). This study investigated whether radiologic parameters obtained by dynamic contrast-enhanced (DCE)-integrated magnetic resonance-positron emission tomography (MR-PET) could be used to quantitatively assess tumor angiogenesis in NSCLC. METHODS: This prospective cohort study included 75 patients with NSCLC who underwent DCE-integrated MR-PET at diagnosis. The following parameters were analyzed: metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax), reverse reflux rate constant (kep), volume transfer constant (Ktrans), blood plasma volume fraction (vp), extracellular extravascular volume fraction (ve), apparent diffusion coefficient (ADC), and initial area under the time-to-signal intensity curve at 60 s post enhancement (iAUC60). Serum biomarkers of tumor angiogenesis, including vascular endothelial growth factor-A (VEGF-A), angiogenin, and angiopoietin-1, were measured by enzyme-linked immunosorbent assays simultaneously. RESULTS: Serum VEGF-A (p = 0.002), angiogenin (p = 0.023), and Ang-1 (p <  0.001) concentrations were significantly elevated in NSCLC patients compared with healthy individuals. MR-PET parameters, including MTV, Ktrans, and kep, showed strong linear correlations (p <  0.001) with serum angiogenesis-related biomarkers. Serum VEGF-A concentrations (p = 0.004), MTV values (p <  0.001), and kep values (p = 0.029) were significantly higher in patients with advanced-stage disease (stage III or IV) than in those with early-stage disease (stage I or II). Patients with initial higher values of angiogenesis-related MR-PET parameters, including MTV > 30 cm3 (p = 0.046), Ktrans > 200 10- 3/min (p = 0.069), and kep > 900 10- 3/min (p = 0.048), may have benefited from angiogenesis inhibitor therapy, which thus led to significantly longer overall survival. CONCLUSIONS: The present findings suggest that DCE-integrated MR-PET provides a reliable, non-invasive, quantitative assessment of tumor angiogenesis; can guide the use of angiogenesis inhibitors toward longer survival; and will play an important role in the personalized treatment of NSCLC.

Experimental robustness of Fourier ptychography phase retrieval algorithms.

Fourier ptychography is a new computational microscopy technique that provides gigapixel-scale intensity and phase images with both wide field-of-view and high resolution. By capturing a stack of low-resolution images under different illumination angles, an inverse algorithm can be used to computationally reconstruct the high-resolution complex field. Here, we compare and classify multiple proposed inverse algorithms in terms of experimental robustness. We find that the main sources of error are noise, aberrations and mis-calibration (i.e. model mis-match). Using simulations and experiments, we demonstrate that the choice of cost function plays a critical role, with amplitude-based cost functions performing better than intensity-based ones. The reason for this is that Fourier ptychography datasets consist of images from both brightfield and darkfield illumination, representing a large range of measured intensities. Both noise (e.g. Poisson noise) and model mis-match errors are shown to scale with intensity. Hence, algorithms that use an appropriate cost function will be more tolerant to both noise and model mis-match. Given these insights, we propose a global Newton's method algorithm which is robust and accurate. Finally, we discuss the impact of procedures for algorithmic correction of aberrations and mis-calibration.

In-line digital holographic imaging in volume holographic microscopy.

A dual-plane in-line digital holographic imaging method incorporating volume holographic microscopy (VHM) is presented to reconstruct objects in a single shot while eliminating zero-order and twin-image diffracted waves. The proposed imaging method is configured such that information from different axial planes is acquired simultaneously using multiplexed volume holographic imaging gratings, as used in VHM, and recorded as in-line holograms where the corresponding reference beams are generated in the fashion of Gabor's in-line holography. Unlike conventional VHM, which can take axial intensity information only at focal depths, the proposed method digitally reconstructs objects at any axial position. Further, we demonstrate the proposed imaging technique's ability to effectively eliminate zero-order and twin images for single-shot three-dimensional object reconstruction.

Syllable or phoneme? A mouse-tracking investigation of phonological units in Mandarin Chinese and English spoken word recognition.

Previous studies on spoken word production have shown that native English speakers used phoneme-sized units (e.g., a word-initial phoneme, C) to produce English words, and native Mandarin Chinese speakers employed syllable-sized units (e.g., a word-initial consonant and vowel, CV) as phonological encoding units in Chinese. With spoken word production, although spoken word recognition is a fundamental language skill of human beings, it is unknown whether a Chinese-English bilingual listener can adjust the phonological unit sizes depending on the language used. Using four mouse-tracking spoken word recognition experiments, participants listened to spoken words (either presented in English or Chinese) while viewing a display of two written words. In Experiment 1, Chinese-English bilinguals experienced a larger phonological competition in the CV condition than the control condition, indicating that they recruited syllables when listening to Chinese monosyllabic words. Experiment 2 extended the results of Experiment 1 with a set of Chinese disyllabic words and further revealed the temporal distribution of syllable overlap of a spoken word constrains phonological competition. In Experiment 3, Chinese-English bilinguals exhibited a greater phonological competition in both CV and C conditions that mirrored those reported with English monolinguals in Experiment 4. Our results across experiments demonstrated that Chinese-English bilinguals employed both phonemes and syllables to recognize English spoken words but exclusively relied on syllables to recognize Chinese words, suggesting that there is flexibility in selecting phonological units when recognizing spoken words in the two languages. Implications for models of monolingual and bilingual spoken recognition are also discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Correlative imaging of the spatio-angular dynamics of biological systems with multimodal instant polarization microscope.

The spatial and angular organization of biological macromolecules is a key determinant, as well as informative readout, of their function. Correlative imaging of the dynamic spatio-angular architecture of cells and organelles is valuable, but remains challenging with current methods. Correlative imaging of spatio-angular dynamics requires fast polarization-, depth-, and wavelength-diverse measurement of intrinsic optical properties and fluorescent labels. We report a multimodal instant polarization microscope (miPolScope) that combines a broadband polarization-resolved detector, automation, and reconstruction algorithms to enable label-free imaging of phase, retardance, and orientation, multiplexed with fluorescence imaging of concentration, anisotropy, and orientation of molecules at diffraction-limited resolution and high speed. miPolScope enabled multimodal imaging of myofibril architecture and contractile activity of beating cardiomyocytes, cell and organelle architecture of live HEK293T and U2OS cells, and density and anisotropy of white and grey matter of mouse brain tissue across the visible spectrum. We anticipate these developments in joint quantitative imaging of density and anisotropy to enable new studies in tissue pathology, mechanobiology, and imaging-based screens.

DynaMorph: self-supervised learning of morphodynamic states of live cells.

A cell's shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible. To enable automated and quantitative analysis of morphodynamic states, we developed DynaMorph-a computational framework that combines quantitative live cell imaging with self-supervised learning. To demonstrate the robustness and utility of this approach, we used DynaMorph to annotate morphodynamic states observed with label-free measurements of optical density and anisotropy of live microglia isolated from human brain tissue. These cells show complex behavior and have varied responses to disease-relevant perturbations. DynaMorph generates quantitative morphodynamic representations that can be used to compare the effects of the perturbations. Using DynaMorph, we identify distinct morphodynamic states of microglia polarization and detect rare transition events between states. The concepts and the methods presented here can facilitate automated discovery of functional states of diverse cellular systems.

Revealing architectural order with quantitative label-free imaging and deep learning.

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.

Microscopy is central to biological research and has enabled scientist to study the structure and dynamics of cells and their components within. Often, fluorescent dyes or trackers are used that can be detected under the microscope. However, this procedure can sometimes interfere with the biological processes being studied. Now, Guo, Yeh, Folkesson et al. have developed a new approach to examine structures within tissues and cells without the need for a fluorescent label. The technique, called QLIPP, uses the phase and polarization of the light passing through the sample to get information about its makeup. A computational model was used to decode the characteristics of the light and to provide information about the density and orientation of molecules in live cells and brain tissue samples of mice and human. This way, Guo et al. were able to reveal details that conventional microscopy would have missed. Then, a type of machine learning, known as 'deep learning', was used to translate the density and orientation images into fluorescence images, which enabled the researchers to predict specific structures in human brain tissue sections. QLIPP can be added as a module to a microscope and its software is available open source. Guo et al. hope that this approach can be used across many fields of biology, for example, to map the connectivity of nerve cells in the human brain or to identify how cells respond to infection. However, further work in automating other aspects, such as sample preparation and analysis, will be needed to realize the full benefits.

Computational structured illumination for high-content fluorescence and phase microscopy.

High-content biological microscopy targets high-resolution imaging across large fields-of-view (FOVs). Recent works have demonstrated that computational imaging can provide efficient solutions for high-content microscopy. Here, we use speckle structured illumination microscopy (SIM) as a robust and cost-effective solution for high-content fluorescence microscopy with simultaneous high-content quantitative phase (QP). This multi-modal compatibility is essential for studies requiring cross-correlative biological analysis. Our method uses laterally-translated Scotch tape to generate high-resolution speckle illumination patterns across a large FOV. Custom optimization algorithms then jointly reconstruct the sample's super-resolution fluorescent (incoherent) and QP (coherent) distributions, while digitally correcting for system imperfections such as unknown speckle illumination patterns, system aberrations and pattern translations. Beyond previous linear SIM works, we achieve resolution gains of 4× the objective's diffraction-limited native resolution, resulting in 700 nm fluorescence and 1.2 µm QP resolution, across a FOV of 2 × 2.7 mm 2 , giving a space-bandwidth product (SBP) of 60 megapixels.

Speckle-structured illumination for 3D phase and fluorescence computational microscopy.

High-content biological microscopy targets high-resolution imaging across large fields-of-view, often achieved by computational imaging approaches. Previously, we demonstrated 2D multimodal high-content microscopy via structured illumination microscopy (SIM) with resolution > 2 × the diffraction limit, using speckle illumination from Scotch tape. In this work, we extend the method to 3D by leveraging the fact that the speckle illumination is in fact a 3D structured pattern. We use both a coherent and an incoherent imaging model to develop algorithms for joint retrieval of the 3D super-resolved fluorescent and complex-field distributions of the sample. Our reconstructed images resolve features beyond the physical diffraction-limit set by the system's objective and demonstrate 3D multimodal imaging with ∼ 0.6 × 0.6 × 6   µ m3 resolution over a volume of ∼ 314 × 500 × 24   µ m3.

Structured illumination microscopy with unknown patterns and a statistical prior.

Structured illumination microscopy (SIM) improves resolution by down-modulating high-frequency information of an object to fit within the passband of the optical system. Generally, the reconstruction process requires prior knowledge of the illumination patterns, which implies a well-calibrated and aberration-free system. Here, we propose a new algorithmic self-calibration strategy for SIM that does not need to know the exact patterns a priori, but only their covariance. The algorithm, termed PE-SIMS, includes a pattern-estimation (PE) step requiring the uniformity of the sum of the illumination patterns and a SIM reconstruction procedure using a statistical prior (SIMS). Additionally, we perform a pixel reassignment process (SIMS-PR) to enhance the reconstruction quality. We achieve 2× better resolution than a conventional widefield microscope, while remaining insensitive to aberration-induced pattern distortion and robust against parameter tuning.