Soft X-ray tomograms provide a structural basis for whole-cell modeling

Automated 3D cytoplasm segmentation in soft X-ray tomography

Cells' structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune T cells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.

Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography

The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.

Soft X-ray Microscopy in Cell Biology: Current Status, Contributions and Prospects

The recent advances achieved in microscopy technology have led to a significant breakthrough in biological research. Super-resolution fluorescent microscopy now allows us to visualize subcellular structures down to the pin-pointing of the single molecules in them, while modern electron microscopy has opened new possibilities in the study of protein complexes in their native, intracellular environment at near-atomic resolution. Nonetheless, both fluorescent and electron microscopy have remained beset by their principal shortcomings: the reliance on labeling procedures and severe sample volume limitations, respectively. Soft X-ray microscopy is a candidate method that can compensate for the shortcomings of both technologies by making possible observation of the entirety of the cellular interior without chemical fixation and labeling with an isotropic resolution of 40-70 nm. This will thus bridge the resolution gap between light and electron microscopy (although this gap is being narrowed, it still exists) and resolve the issue of compatibility with the former, and possibly in the near future, the latter methods. This review aims to assess the current state of soft X-ray microscopy and its impact on our understanding of the subcellular organization. It also attempts to look into the future of X-ray microscopy, particularly as relates to its seamless integration into the cell biology toolkit.

Maximizing microbial bioproduction from sustainable carbon sources using iterative systems engineering

Maximizing the production of heterologous biomolecules is a complex problem that can be addressed with a systems-level understanding of cellular metabolism and regulation. Specifically, growth-coupling approaches can increase product titers and yields and also enhance production rates. However, implementing these methods for non-canonical carbon streams is challenging due to gaps in metabolic models. Over four design-build-test-learn cycles, we rewire Pseudomonas putida KT2440 for growth-coupled production of indigoidine from para-coumarate. We explore 4,114 potential growth-coupling solutions and refine one design through laboratory evolution and ensemble data-driven methods. The final growth-coupled strain produces 7.3 g/L indigoidine at 77% maximum theoretical yield in para-coumarate minimal medium. The iterative use of growth-coupling designs and functional genomics with experimental validation was highly effective and agnostic to specific hosts, carbon streams, and final products and thus generalizable across many systems.

Dehydration as alternative sample preparation for soft X-ray tomography

Soft X-ray tomography (SXT) is an imaging technique to visualise whole cells without fixation, staining, and sectioning. For SXT imaging, cells are cryopreserved and imaged at cryogenic conditions. Such 'near-to-native' state imaging is in high demand and initiated the development of the laboratory table-top SXT microscope. As many laboratories do not have access to cryogenic equipment, we asked ourselves whether SXT imaging is feasible on dry specimens. This paper shows how the dehydration of cells can be used as an alternative sample preparation to obtain ultrastructure information. We compare different dehydration processes on mouse embryonic fibroblasts in terms of ultrastructural preservation and shrinkage. Based on this analysis, we chose critical point (CPD) dried cells for SXT imaging. In comparison to cryopreserved and air-dried cells, CPD dehydrated cells show high structural integrity although with about 3-7 times higher X-ray absorption for cellular organelles. As the difference in X-ray absorption values between organelles is preserved, 3D anatomy of CPD-dried cells can be segmented and analysed, demonstrating the applicability of CPD-dried sample preparation for SXT imaging. LAY DESCRIPTION: Soft X-ray tomography (SXT) is an imaging technique that allows to see the internal structures of cells without the need for special treatments like fixation or staining. Typically, SXT imaging involves freezing and imaging cells at very low temperatures. However, since many labs lack the necessary equipment, we explored whether SXT imaging could be done on dry samples instead. We compared different dehydration methods and found that critical point drying (CPD) was the most promising for SXT imaging. CPD-dried cells showed high structural integrity, although they absorbed more X-rays than hydrated cells, demonstrating that CPD-dried sample preparation is a viable alternative for SXT imaging.

Bayesian methods in integrative structure modeling

There is a growing interest in characterizing the structure and dynamics of large biomolecular assemblies and their interactions within the cellular environment. A diverse array of experimental techniques allows us to study biomolecular systems on a variety of length and time scales. These techniques range from imaging with light, X-rays or electrons, to spectroscopic methods, cross-linking mass spectrometry and functional genomics approaches, and are complemented by AI-assisted protein structure prediction methods. A challenge is to integrate all of these data into a model of the system and its functional dynamics. This review focuses on Bayesian approaches to integrative structure modeling. We sketch the principles of Bayesian inference, highlight recent applications to integrative modeling and conclude with a discussion of current challenges and future perspectives.

Biological function investigated by time-resolved structure determination

Inspired by recent progress in time-resolved x-ray crystallography and the adoption of time-resolution by cryo-electronmicroscopy, this article enumerates several approaches developed to become bigger/smaller, faster, and better to gain new insight into the molecular mechanisms of life. This is illustrated by examples where chemical and physical stimuli spawn biological responses on various length and time-scales, from fractions of Ångströms to micro-meters and from femtoseconds to hours.