Biology subjects

La Riviere, P.

Publications and source records attributed to La Riviere, P..

11 recordsLinked to original sources

bioRxiv2026

Label-free 3D virtual histology of human formalin-fixed paraffin-embedded (FFPE) prostate needle biopsies with propagation-based phase-contrast micro-CT (PBCT)

For over a century, the goal of estimating clinical outcome from tumor biopsies has been based on histomorphology of 2D tissue slices that represent a small fraction of collected samples. Its power derives from histologys 1) unbiased representation of cell types, 2) subcellular resolution that allows the characterization of health and disease states across cell types, and 3) multi-millimeter fields of view that allow assessment of tumor heterogeneity. Histologys dependence upon physical slices, however, limits assessment of 3-dimensional cellular volumes and tissue architecture. Here, we used propagation-based phase-contrast micro-CT (PBCT) to create 3D histological images of residual formalin-fixed, paraffin-embedded (FFPE) prostate needle biopsies. The resulting isotropic, grey-scale, 0.5 micron voxel matrices were used to explore the potential of for the 3D virtual histology to distinguish diagnostic categories including benign prostatic tissue and prostatic adenocarcinoma of Gleason patterns 3, 4, and 5. Maximum intensity projections of stacks of digital slices totaling 5 microns "slices" allowed the study of virtual sections corresponding to actual serial H&E-stained sections of tissue cut after micro-CT imaging. Like histology, our PBCT reconstructions allowed us to distinguish between non-infiltrative and undulating glands of benign prostatic tissue, infiltrative round glands of Gleason pattern 3, cribriform structures of Gleason pattern 4, and comedonecrosis of Gleason pattern 5. Unlike histology, micro-CT allowed us to further probe 3D tissue architecture in volumetric context. User-friendly exploration of sample volumes was achieved using a customized Neuroglancer multiplanar and 3D rendering interface. Sparsely trained cycleGAN produced plausible virtual H&E staining from the unstained micro-CT reconstructions. Unlike tissue-section based histology, micro-CT-based virtual histology yields nondestructive 3D characterization of cancer cell and tissue architecture, including glandular spaces, without the undersampling or cutting artifacts of histology. These findings demonstrate the feasibility of PBCT-based 3D virtual histology of prostate cancer and suggest the exploration of derived quantitative analyses of tumor properties for potential contributions to patient care.

pathology↗

bioRxiv2026

Long-range neural pathways for octopus chemotactile processing revealed from periphery-to-brain by centimeter-field microCT

Understanding how nervous systems mediate responses to sensation requires whole-body maps of periphery-to-brain connections. Octopuses exemplify this challenge with distributed control of eight arms and hundreds of suckers, yet their long-range microanatomical wiring remains elusive due to limitations in microscopy. We extend histotomography (Ding et al. 2019), a form of soft tissue microCT customized for volumetric characterization of cells and tissues, to centimeter range with a custom micro-CT imaging system (Ding et al., 2019). With its 10-mm field of view and 0.7-{micro}m isotropic voxels we created a high-resolution digital intact small octopus. This multi-tissue 3D blueprint enabled us to (i) elucidate previously uncharacterized chemotactile pathways from the suckers to the brain, (ii) discern subdivisions of the nerve ring connecting neighboring arms, and (iii) segment over 300 structures across organ systems at histology-like resolution. We release the labeled interactive digital specimen to facilitate collaborative whole-organism phenotyping as a practical foundation for digital organismal biology. ONE-SENTENCE SUMMARYWhole-body 3D histology reveals neural and organ architecture throughout a small octopus.

systems biology↗

bioRxiv2024

Three-dimensional spatio-angular fluorescence microscopy with a polarized dual-view inverted selective-plane illumination microscope (pol-diSPIM)

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the three-dimensional orientations and diffraction-limited positions of ensembles of fluorescent dipoles that label biological structures, and we share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model our samples, their excitation, and their detection using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labelled giant unilamellar vesicles, fast-scarlet-labelled cellulose in xylem cells, and phalloidin-labelled actin in U2OS cells. Additionally, we observe phalloidin-labelled actin in mouse fibroblasts grown on grids of labelled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.

biophysics↗

bioRxiv2023

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained de-aberration networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

bioengineering↗

bioRxiv2023

Sinogram Domain Angular Upsampling of Sparse-View Micro-CT with Dense Residual Hierarchical Transformer and Noise-Aware Loss

Reduced angular sampling is a key strategy for increasing scanning efficiency of micron-scale computed tomography (micro-CT). Despite boosting throughput, this strategy introduces noise and artifacts due to undersampling. In this work, we present a solution to this issue, by proposing a novel Dense Residual Hierarchical Transformer (DRHT) network to recover high-quality sinograms from 2 x, 4x and 8x undersampled scans. DRHT is trained to utilize limited information available from sparsely angular sampled scans and once trained, it can be applied to recover higher-resolution sinograms from shorter scan sessions. Our proposed DRHT model aggregates the benefits of a hierarchical-multi-scale structure along with the combination of local and global feature extraction through dense residual convolutional blocks and non-overlapping window transformer blocks respectively. We also propose a novel noise-aware loss function named KL-L1 to improve sinogram restoration to full resolution. KL-L1, a weighted combination of pixel-level and distribution-level cost functions, leverages inconsistencies in noise distribution and uses learnable spatial weights to improve the training of the DRHT model. We present ablation studies and evaluations of our method against other state-of-the-art (SOTA) models over multiple datasets. Our proposed DRHT network achieves an average increase in peak signal to noise ratio (PSNR) of 17.73dB and a structural similarity index (SSIM) of 0.161, for 8x upsampling, across the three unique datasets, compared to their respective Bicubic interpolated versions. This novel approach can be utilized to decrease radiation exposure to patients and reduce imaging time for large-scale CT imaging projects.

bioengineering↗

bioRxiv2022

Three-dimensional structured illumination microscopy with enhanced axial resolution

We present two distinct, complementary methods for improving axial resolution in three-dimensional structured illumination microscopy (3D SIM) with minimal or no modification to the optical system. First, we show that placing a mirror directly opposite the sample enables 4-beam interference with higher spatial frequency content than 3D SIM illumination, offering near-isotropic imaging with [~]120 nm lateral and 160 nm axial resolution. Second, we develop an improved deep learning method that can be directly applied to 3D SIM data, obviating the need for additional hardware. This procedure results in [~]120 nm isotropic resolution and can be combined with denoising to facilitate volumetric imaging spanning dozens of time points. We demonstrate the potential of these advances by imaging a variety of cellular samples, delineating the nanoscale distribution of vimentin and microtubule filaments, observing the relative positions of caveolar coat proteins and lysosomal markers, and visualizing rich cytoskeletal dynamics within T-cells in the early stages of immune synapse formation.

cell biology↗

bioRxiv2022

Model-free analysis in the spectral domain of postmortem mouse brain EPSI reveals inconsistencies with model-based analyses of the free induction decay

PurposeDysmyelinating disorders lead to abnormalities in myelin structure that produce detectable effects in an echo-planar spectroscopic imaging (EPSI) signal. To estimate the voxel-wise proportion of myelin, data are typically fit to compartmental models in the time domain. This work characterizes limitations in these models by comparing high-resolution water spectra measured in postmortem fixed mouse brains to spectra predicted from time-domain models fit to the same data, specifically by comparing spectra from control and shiverer mice, a model for dysmyelination. MethodsPerfusion-fixed, resected control (n = 5) and shiverer (n = 4) mouse brains were imaged using 3D EPSI with 100 {micro}m isotropic resolution. The free induction decay (FID) was sampled every 2.74 ms over 192 echoes and Fourier transformed to produce water spectra with 1.9 Hz resolution. FIDs were also fit to two biophysical models and the resulting fits were converted to spectra with a Fourier transform. Spectral asymmetry was computed and compared before and after fitting the data to models. ResultsSpectra derived from both models did not show the magnitude of asymmetric broadening observed in the raw data. Correlations between data- and model-derived asymmetries and estimated frequency shifts are weak, leading to a reduction in spectral sensitivity to changes in white-matter structure after fitting the data to models. ConclusionThe results demonstrate spectral inconsistencies between biophysical model predictions and measured data, promoting the further incorporation of spectral analysis methods to develop and benchmark new model-based approaches.

biophysics↗

bioRxiv2021

Multiview super-resolution microscopy

We enhance the performance of confocal microscopy over imaging scales spanning tens of nanometers to millimeters in space and milliseconds to hours in time, improving volumetric resolution more than 10-fold while simultaneously reducing phototoxicity. We achieve these gains via an integrated, four-pronged approach: 1) developing compact line-scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; 2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; 3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labeled, thick samples; 4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. We demonstrate these capabilities on more than twenty distinct fixed and live samples, including protein distributions in single cells; nuclei and developing neurons in Caenorhabditis elegans embryos, larvae, and adults; myoblasts in Drosophila wing imaginal disks; and mouse renal, esophageal, cardiac, and brain tissues.

bioengineering↗

bioRxiv2020

Multi-modal imaging of a single postmortem mouse brain over five orders of magnitude of resolution

Mammalian neurons operate at length scales spanning five orders of magnitude; micron-scale-diameter myelinated axons project millimeters across brain regions, ultimately forming nanometer scale synapses on individual post-synaptic neurons. Capturing these anatomical features across that breadth of scale has required imaging samples with multiple independent imaging modalities (e.g. MRI, electron microscopy, etc.). Translating between the different modalities, however, requires imaging the same brain with each. Here, we imaged the same postmortem mouse brain over five orders of spatial resolution using MRI, whole brain micron-scale synchrotron x-ray tomography (CT), and large volume automated serial electron microscopy. Using this pipeline, we can track individual myelinated axons previously relegated to axon bundles in diffusion tensor MRI or arbitrarily trace neurons and their processes brain-wide and identify individual synapses on them. This pipeline provides both an unprecedented look across a single brains multi-scaled organization as well as a vehicle for studying the brains multi-scale pathologies.

neuroscience↗

bioRxiv2018

Three-Dimensional Histology of Whole Zebrafish by Sub-Micron Synchrotron X-ray Micro-Tomography

Histological studies providing cellular insights into tissue architecture have been central to biological discovery and remain clinically invaluable today. Extending histology to three dimensions would be transformational for research and diagnostics. However, three-dimensional histology is impractical using current techniques. We have customized sample preparation, synchrotron X-ray tomographic parameters, and three-dimensional image analysis to allow for complete histological phenotyping using whole larval and juvenile zebrafish. The resulting digital zebrafish can be virtually sectioned and visualized in any plane. Whole-animal reconstructions at subcellular resolution also enable computational characterization of the zebrafish nervous system by region-specific detection of cell nuclei and quantitative assessment of individual phenotypic variation. Three-dimensional histological phenotyping has potential use in genetic and chemical screens, and in clinical and toxicological tissue diagnostics.\n\nOne Sentence SummarySynchrotron X-ray micro-tomography can be used to rapidly create 3-dimensional images of fixed and stained specimens without sectioning, enabling computational histological phenotyping at cellular resolution.

pathology↗

bioRxiv2017

Reflective imaging improves resolution, speed, andcollection efficiency in light sheet microscopy

Light-sheet fluorescence microscopy (LSFM) enables high-speed, high-resolution, gentle imaging of live biological specimens over extended periods. Here we describe a technique that improves the spatiotemporal resolution and collection efficiency of LSFM without modifying the underlying microscope. By imaging samples on reflective coverslips, we enable simultaneous collection of multiple views, obtaining 4 complementary views in 250 ms, half the period it would otherwise take to collect only two views in symmetric dual-view selective plane illumination microscopy (diSPIM). We also report a modified deconvolution algorithm that removes the associated epifluorescence contamination and fuses all views for resolution recovery. Furthermore, we enhance spatial resolution (to < 300 nm in all three dimensions) by applying our method to a new asymmetric diSPIM, permitting simultaneous acquisition of two high-resolution views otherwise difficult to obtain due to steric constraints at high numerical aperture (NA). We demonstrate the broad applicability of our method in a variety of samples of moderate (< 50 m) thickness, studying mitochondrial, membrane, Golgi, and microtubule dynamics in single cells and calcium activity in nematode embryos.

bioengineering↗