Improved Determination of Subnuclear Position Enabled by Three-Dimensional Membrane Reconstruction

Extracting nanoscale membrane morphology from single-molecule localizations

Membrane surface reconstruction at the nanometer scale is required for understanding mechanisms of subcellular shape change. This historically has been the domain of electron microscopy, but extraction of surfaces from specific labels is a difficult task in this imaging modality. Existing methods for extracting surfaces from fluorescence microscopy have poor resolution or require high-quality super-resolution data that are manually cleaned and curated. Here, we present NanoWrap, a new method for extracting surfaces from generalized single-molecule localization microscopy data. This makes it possible to study the shape of specifically labeled membranous structures inside cells. We validate NanoWrap using simulations and demonstrate its reconstruction capabilities on single-molecule localization microscopy data of the endoplasmic reticulum and mitochondria. NanoWrap is implemented in the open-source Python Microscopy Environment.

Extracting nanoscale membrane morphology from single-molecule localizations

Membrane surface reconstruction at the nanometer scale is required for understanding mechanisms of subcellular shape change. This historically has been the domain of electron microscopy, but extraction of surfaces from specific labels is a difficult task in this imaging modality. Existing methods for extracting surfaces from fluorescence microscopy have poor resolution or require high-quality super-resolution data that is manually cleaned and curated. Here we present a new method for extracting surfaces from generalized single-molecule localization microscopy (SMLM) data. This makes it possible to study the shape of specifically-labelled membraneous structures inside of cells. We validate our method using simulations and demonstrate its reconstruction capabilities on SMLM data of the endoplasmic reticulum and mitochondria. Our method is implemented in the open-source Python Microscopy Environment.

SIGNIFICANCE

We introduce a novel tool for reconstruction of subcellular membrane surfaces from single-molecule localization microscopy data and use it to visualize and quantify local shape and membrane-membrane interactions. We benchmark its performance on simulated data and demonstrate its fidelity to experimental data.

High-Resolution Microscopy to Learn the Nuclear Organization of the Living Yeast Cells

The spatial organization of the nucleus is a key determinant in all genome activities. However, the accurate measurement of the nuclear organization is still technically challenging. Here, the technology NucQuant we created previously was utilized to detect the variation of the nuclear organization, including the heterogeneity of the nuclear geometry, the change of the NPC distribution along different cell cycle stages during interphase, and the organization of the nucleolus. The results confirmed that not only the growth rate and the NPC distribution are influenced by the carbon source; the nuclear shape is also impacted by the carbon source. The nuclei lost their spherical geometry gradually when the cell was cultured from the most to a less favorable carbon source. We also discovered that the nucleolus prefers to locate at the nuclear periphery, which was called the "genes poor region," especially when the cells entered quiescence. Furthermore, the distribution of the NPC along the different stages during the interphase was analyzed. We proposed that with the growth of the cell, the nucleus would grow from the surface of the NE flanking the nucleolus firstly.

A versatile image analysis platform for three-dimensional nuclear reconstruction

Nuclear morphology is indicative of cellular health in many contexts. In order to robustly and quantitatively measure nuclear size and shape, numerous experimental methods leveraging fluorescence microscopy have been developed. While these methods are useful for quantifying two-dimensional morphology, they often fail to accurately represent the three-dimensional structure of the nucleus, thus omitting important spatial and volumetric information. To address the need for a more accurate image analysis modality, we have developed a software platform that faithfully reconstructs membrane surfaces in three dimensions with sub-pixel resolution. Here, we showcase its broad applicability across species and nuclear scale, as well as provide information on how to employ this platform for diverse experimental systems.

High resolution microscopy reveals the nuclear shape of budding yeast during cell cycle and in various biological states

How spatial organization of the genome depends on nuclear shape is unknown, mostly because accurate nuclear size and shape measurement is technically challenging. In large cell populations of the yeast Saccharomyces cerevisiae, we assessed the geometry (size and shape) of nuclei in three dimensions with a resolution of 30 nm. We improved an automated fluorescence localization method by implementing a post-acquisition correction of the spherical microscopic aberration along the z-axis, to detect the three dimensional (3D) positions of nuclear pore complexes (NPCs) in the nuclear envelope. Here, we used a method called NucQuant to accurately estimate the geometry of nuclei in 3D throughout the cell cycle. To increase the robustness of the statistics, we aggregated thousands of detected NPCs from a cell population in a single representation using the nucleolus or the spindle pole body (SPB) as references to align nuclei along the same axis. We could detect asymmetric changes of the nucleus associated with modification of nucleolar size. Stereotypical modification of the nucleus toward the nucleolus further confirmed the asymmetric properties of the nuclear envelope.

Summary: This novel method to explore 3D geometry of the nuclear envelope with enhanced resolution and post-acquisition correction of z-axis aberration revealed increased NPC density near the SPB and the nucleolus.