SuRVoS 2: Accelerating Annotation and Segmentation for Large Volumetric Bioimage Workflows Across Modalities and Scales

VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles

Cochlear hair cells are essential for hearing, and their stereocilia bundles are critical for mechanotransduction. However, analyzing the 3D morphology of these bundles can be challenging due to their complex organization and the presence of other cellular structures in the tissue. To address this, we developed VASCilia (Vision Analysis StereoCilia), a Napari plugin suite that automates the analysis of 3D confocal microscopy datasets of phalloidin-stained cochlear hair cell bundles. VASCilia includes five deep learning-based models that streamline the analysis process, including: (1) Z-Focus Tracker (ZFT) for selecting relevant slices in a 3D image stack; (2) PCPAlignNet (Planar Cell Polarity Alignment Network) for automated orientation of image stacks; (3) a segmentation model for identifying and delineating stereocilia bundles; (4) a tonotopic Position Prediction tool; and (5) a classification tool for identifying hair cell subtypes. In addition, VASCilia provides automated computational tools and measurement capabilities. Using VASCilia, we found that the total actin content of stereocilia bundles (as measured by phalloidin staining) does not necessarily increase with bundle height, which is likely due to differences in stereocilia thickness and number. This novel biological finding demonstrates the power of VASCilia in facilitating detailed quantitative analysis of stereocilia. VASCilia also provides a user-friendly interface that allows researchers to easily navigate and use the tool, with the added capability to reload all their analyses for review or sharing purposes. We believe that VASCilia will be a valuable resource for researchers studying cochlear hair cell development and function, addressing a longstanding need in the hair cell research community for specialized deep learning-based tools capable of high-throughput image quantitation. We have released our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation (https://github.com/ucsdmanorlab/Napari-VASCilia). This dataset comprises a total of 502 inner and 1,703 outer hair cell bundles annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to accelerate advances within the hearing research community.

Structural glycobiology - from enzymes to organelles

Biological carbohydrate polymers represent some of the most complex molecules in life, enabling their participation in a huge range of physiological functions. The complexity of biological carbohydrates arises from an extensive enzymatic repertoire involved in their construction, deconstruction and modification. Over the past decades, structural studies of carbohydrate processing enzymes have driven major insights into their mechanisms, supporting associated applications across medicine and biotechnology. Despite these successes, our understanding of how multienzyme networks function to create complex polysaccharides is still limited. Emerging techniques such as super-resolution microscopy and cryo-electron tomography are now enabling the investigation of native biological systems at near molecular resolutions. Here, we review insights from classical in vitro studies of carbohydrate processing, alongside recent in situ studies of glycosylation-related processes. While considerable technical challenges remain, the integration of molecular mechanisms with true biological context promises to transform our understanding of carbohydrate regulation, shining light upon the processes driving functional complexity in these essential biomolecules.

An Integrated Approach to Elucidate the Interplay between Iron Uptake Dynamics and Magnetosome Formation at the Single-Cell Level in Magnetospirillum gryphiswaldense

Iron is a crucial element integral to various fundamental biological molecular mechanisms, including magnetosome biogenesis in magnetotactic bacteria (MTB). Magnetosomes are formed through the internalization and biomineralization of iron into magnetite crystals. However, the interconnected mechanisms by which MTB uptake and regulate intracellular iron for magnetosome biomineralization remain poorly understood, particularly at the single-cell level. To gain insights we employed a holistic multiscale approach, i.e., from elemental iron species to bacterial populations, to elucidate the interplay between iron uptake dynamics and magnetosome formation in Magnetospirillum gryphiswaldense MSR-1 under near-native conditions. We combined a correlative microscopy approach integrating light and X-ray tomography with analytical techniques, such as flow cytometry and inductively coupled plasma spectroscopy, to evaluate the effects of iron and oxygen availability on cellular growth, magnetosome biogenesis, and intracellular iron pool in MSR-1. Our results revealed that increased iron availability under microaerobic conditions significantly promoted the formation of longer magnetosome chains and increased intracellular iron uptake, with a saturation point at 300 μM iron citrate. Beyond this threshold, additional iron did not further extend the magnetosome chain length or increase total intracellular iron levels. Moreover, our work reveals (i) a direct correlation between the labile Fe2+ pool size and magnetosome content, with higher intracellular iron concentrations correlating with increased magnetosome production, and (ii) the existence of an intracellular iron pool, distinct from magnetite, persisting during all stages of biomineralization. This study offers insights into iron dynamics in magnetosome biomineralization at a single-cell level, potentially enhancing the industrial biomanufacturing of magnetosomes.

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.

Statistical Effective Diffusivity Estimation in Porous Media Using an Integrated On-site Imaging Workflow for Synchrotron Users

Transport in porous media plays an essential role for many physical, engineering, biological and environmental processes. Novel synchrotron imaging techniques and image-based models have enabled more robust quantification of geometric structures that influence transport through the pore space. However, image-based modelling is computationally expensive, and end users often require, while conducting imaging campaign, fast and agile bulk-scale effective parameter estimates that account for the pore-scale details. In this manuscript we enhance a pre-existing image-based model solver known as OpenImpala to estimate bulk-scale effective transport parameters. In particular, the boundary conditions and equations in OpenImpala were modified in order to estimate the effective diffusivity in an imaged system/geometry via a formal multi-scale homogenisation expansion. Estimates of effective pore space diffusivity were generated for a range of elementary volume sizes to estimate when the effective diffusivity values begin to converge to a single value. Results from OpenImpala were validated against a commercial finite element method package COMSOL Multiphysics (abbreviated as COMSOL). Results showed that the effective diffusivity values determined with OpenImpala were similar to those estimated by COMSOL. Tests on larger domains comparing a full image-based model to a homogenised (geometrically uniform) domain that used the effective diffusivity parameters showed differences below 2 % error, thus verifying the accuracy of the effective diffusivity estimates. Finally, we compared OpenImpala's parallel computing speeds to COMSOL. OpenImpala consistently ran simulations within fractions of minutes, which was two orders of magnitude faster than COMSOL providing identical supercomputing specifications. In conclusion, we demonstrated OpenImpala's utility as part of an on-site tomography processing pipeline allowing for fast and agile assessment of porous media processes and to guide imaging campaigns while they are happening at synchrotron beamlines.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11242-023-01993-7.

Okapi-EM: A napari plugin for processing and analyzing cryogenic serial focused ion beam/scanning electron microscopy images

An emergent volume electron microscopy technique called cryogenic serial plasma focused ion beam milling scanning electron microscopy (pFIB/SEM) can decipher complex biological structures by building a three-dimensional picture of biological samples at mesoscale resolution. This is achieved by collecting consecutive SEM images after successive rounds of FIB milling that expose a new surface after each milling step. Due to instrumental limitations, some image processing is necessary before 3D visualization and analysis of the data is possible. SEM images are affected by noise, drift, and charging effects, that can make precise 3D reconstruction of biological features difficult. This article presents Okapi-EM, an open-source napari plugin developed to process and analyze cryogenic serial pFIB/SEM images. Okapi-EM enables automated image registration of slices, evaluation of image quality metrics specific to pFIB-SEM imaging, and mitigation of charging artifacts. Implementation of Okapi-EM within the napari framework ensures that the tools are both user- and developer-friendly, through provision of a graphical user interface and access to Python programming.

A protocol for cryogenic volumetric imaging using serial plasma FIB/SEM

Cryogenic volumetric imaging using serial plasma focused ion beam scanning electron microscopy (serial pFIB/SEM) is a new and exciting correlative volume electron microscopy (vEM) technique. It enables visualization of un-stained, cryogenically immobilized cells and tissues with ∼20-50nm resolution and a field of view of ∼10-30μm resulting in near-native state imaging and the possibility of microscale, mesoscale and nanoscale correlative imaging. We have written a detailed protocol for optimization of FIB and SEM parameters to reduce imaging artefacts and enable downstream computational processing and analysis. While our experience is based on use of a single system, the protocol has been written to be as hardware and software agnostic as possible, with a focus on the purpose of each step rather than a fully procedural description to provide a useful resource regardless of the system/software in use.

Cryo-plasma FIB/SEM volume imaging of biological specimens

Serial focussed ion beam scanning electron microscopy (FIB/SEM) enables imaging and assessment of subcellular structures on the mesoscale (10 nm to 10 µm). When applied to vitrified samples, serial FIB/SEM is also a means to target specific structures in cells and tissues while maintaining constituents' hydration shells for in situ structural biology downstream. However, the application of serial FIB/SEM imaging of non-stained cryogenic biological samples is limited due to low contrast, curtaining, and charging artefacts. We address these challenges using a cryogenic plasma FIB/SEM. We evaluated the choice of plasma ion source and imaging regimes to produce high-quality SEM images of a range of different biological samples. Using an automated workflow we produced three-dimensional volumes of bacteria, human cells, and tissue, and calculated estimates for their resolution, typically achieving 20-50 nm. Additionally, a tag-free localisation tool for regions of interest is needed to drive the application of in situ structural biology towards tissue. The combination of serial FIB/SEM with plasma-based ion sources promises a framework for targeting specific features in bulk-frozen samples (>100 µm) to produce lamellae for cryogenic electron tomography.