3D membrane segmentation and quantification of intact thick cells using cryo soft X-ray transmission microscopy: A pilot study

UNILIPID, a Methodology for Energetically Accurate Prediction of Protein Insertion into Implicit Membranes of Arbitrary Shape

The insertion of proteins into membranes is crucial for understanding their function in many biological processes. In this work, we present UNILIPID, a universal implicit lipid-protein description as a methodology for dealing with implicit membranes. UNILIPID is independent of the scale of representation and can be applied at the level of all atoms, coarse-grained particles down to the level of a single bead per amino acid. We provide example implementations for these scales and demonstrate the versatility of our approach by accurately reflecting the free energy of transfer for each amino acid. In addition to single membranes, we describe the analytical implementation of double membranes and show that UNILIPID is well suited for modeling at multiple scales. We generalize to membranes of arbitrary shape. With UNILIPID, we provide a methodological framework for a simple and general parameterization tuned to reproduce a selected reference hydrophobicity scale. The software we provide along with the methodological description is optimized for specific user features such as real-time response, live visual analysis, and virtual reality experiences.

Imaging cell morphology and physiology using X-rays

Morphometric measurements, such as quantifying cell shape, characterizing sub-cellular organization, and probing cell-cell interactions, are fundamental in cell biology and clinical medicine. Until quite recently, the main source of morphometric data on cells has been light- and electron-based microscope images. However, many technological advances have propelled X-ray microscopy into becoming another source of high-quality morphometric information. Here, we review the status of X-ray microscopy as a quantitative biological imaging modality. We also describe the combination of X-ray microscopy data with information from other modalities to generate polychromatic views of biological systems. For example, the amalgamation of molecular localization data, from fluorescence microscopy or spectromicroscopy, with structural information from X-ray tomography. This combination of data from the same specimen generates a more complete picture of the system than that can be obtained by a single microscopy method. Such multimodal combinations greatly enhance our understanding of biology by combining physiological and morphological data to create models that more accurately reflect the complexities of life.

Increasing cell-device adherence using cultured insect cells for receptor-based biosensors

Field-effect transistor (FET)-based biosensors have a wide range of applications, and a bio-FET odorant sensor, based on insect (Sf21) cells expressing insect odorant receptors (ORs) with sensitivity and selectivity, has emerged. To fully realize the practical application of bio-FET odorant sensors, knowledge of the cell-device interface for efficient signal transfer, and a reliable and low-cost measurement system using the commercial complementary metal-oxide semiconductor (CMOS) foundry process, will be indispensable. However, the interfaces between Sf21 cells and sensor devices are largely unknown, and electrode materials used in the commercial CMOS foundry process are generally limited to aluminium, which is reportedly toxic to cells. In this study, we investigated Sf21 cell-device interfaces by developing cross-sectional specimens. Calcium imaging of Sf21 cells expressing insect ORs was used to verify the functions of Sf21 cells as odorant sensor elements on the electrode materials. We found that the cell-device interface was approximately 10 nm wide on average, suggesting that the adhesion mechanism of Sf21 cells may differ from that of other cells. These results will help to construct accurate signal detection from expressed insect ORs using FETs.

RAZA: A Rapid 3D z-crossings algorithm to segment electron tomograms and extract organelles and macromolecules

Resolving the 3D architecture of cells to atomic resolution is one of the most ambitious challenges of cellular and structural biology. Central to this process is the ability to automate tomogram segmentation to identify sub-cellular components, facilitate molecular docking and annotate detected objects with associated metadata. Here we demonstrate that RAZA (Rapid 3D z-crossings algorithm) provides a robust, accurate, intuitive, fast, and generally applicable segmentation algorithm capable of detecting organelles, membranes, macromolecular assemblies and extrinsic membrane protein domains. RAZA defines each continuous contour within a tomogram as a discrete object and extracts a set of 3D structural fingerprints (major, middle and minor axes, surface area and volume), enabling selective, semi-automated segmentation and object extraction. RAZA takes advantage of the fact that the underlying algorithm is a true 3D edge detector, allowing the axes of a detected object to be defined, independent of its random orientation within a cellular tomogram. The selectivity of object segmentation and extraction can be controlled by specifying a user-defined detection tolerance threshold for each fingerprint parameter, within which segmented objects must fall and/or by altering the number of search parameters, to define morphologically similar structures. We demonstrate the capability of RAZA to selectively extract subgroups of organelles (mitochondria) and macromolecular assemblies (ribosomes) from cellular tomograms. Furthermore, the ability of RAZA to define objects and their contours, provides a basis for molecular docking and rapid tomogram annotation.