Model based precision structural measurements on barely resolved objects

Computational modeling of cellular structures using conditional deep generative networks

MOTIVATION

Cellular function is closely related to the localizations of its sub-structures. It is, however, challenging to experimentally label all sub-cellular structures simultaneously in the same cell. This raises the need of building a computational model to learn the relationships among these sub-cellular structures and use reference structures to infer the localizations of other structures.

RESULTS

We formulate such a task as a conditional image generation problem and propose to use conditional generative adversarial networks for tackling it. We employ an encoder-decoder network as the generator and propose to use skip connections between the encoder and decoder to provide spatial information to the decoder. To incorporate the conditional information in a variety of different ways, we develop three different types of skip connections, known as the self-gated connection, encoder-gated connection and label-gated connection. The proposed skip connections are built based on the conditional information using gating mechanisms. By learning a gating function, the network is able to control what information should be passed through the skip connections from the encoder to the decoder. Since the gate parameters are also learned automatically, we expect that only useful spatial information is transmitted to the decoder to help image generation. We perform both qualitative and quantitative evaluations to assess the effectiveness of our proposed approaches. Experimental results show that our cGAN-based approaches have the ability to generate the desired sub-cellular structures correctly. Our results also demonstrate that the proposed approaches outperform the existing approach based on adversarial auto-encoders, and the new skip connections lead to improved performance. In addition, the localizations of generated sub-cellular structures by our approaches are consistent with observations in biological experiments.

AVAILABILITY AND IMPLEMENTATION

The source code and more results are available at https://github.com/divelab/cgan/.

Super-Resolution Microscopy of Chromatin

Since the advent of super-resolution microscopy, countless approaches and studies have been published contributing significantly to our understanding of cellular processes. With the aid of chromatin-specific fluorescence labeling techniques, we are gaining increasing insight into gene regulation and chromatin organization. Combined with super-resolution imaging and data analysis, these labeling techniques enable direct assessment not only of chromatin interactions but also of the function of specific chromatin conformational states.

Simultaneously Measuring Image Features and Resolution in Live-Cell STED Images

Reliable interpretation and quantification of cellular features in fluorescence microscopy requires an accurate estimate of microscope resolution. This is typically obtained by measuring the image of a nonbiological proxy for a point-like object, such as a fluorescent bead. Although appropriate for confocal microscopy, bead-based measurements are problematic for stimulated emission depletion microscopy and similar techniques where the resolution depends critically on the choice of fluorophore and acquisition parameters. In this article, we demonstrate that for a known geometry (e.g., tubules), the resolution can be measured in situ by fitting a model that accounts for both the point spread function (PSF) and the fluorophore distribution. To address the problem of coupling between tubule diameter and PSF width, we developed a technique called nested-loop ensemble PSF fitting. This approach enables extraction of the size of cellular features and the PSF width in fixed-cell and live-cell images without relying on beads or precalibration. Nested-loop ensemble PSF fitting accurately recapitulates microtubule diameter from stimulated emission depletion images and can measure the diameter of endoplasmic reticulum tubules in live COS-7 cells. Our algorithm has been implemented as a plugin for the PYthon Microscopy Environment, a freely available and open-source software.

Super-resolution microscopy approaches to nuclear nanostructure imaging

The human genome has been decoded, but we are still far from understanding the regulation of all gene activities. A largely unexplained role in these regulatory mechanisms is played by the spatial organization of the genome in the cell nucleus which has far-reaching functional consequences for gene regulation. Until recently, it appeared to be impossible to study this problem on the nanoscale by light microscopy. However, novel developments in optical imaging technology have radically surpassed the limited resolution of conventional far-field fluorescence microscopy (ca. 200nm). After a brief review of available super-resolution microscopy (SRM) methods, we focus on a specific SRM approach to study nuclear genome structure at the single cell/single molecule level, Spectral Precision Distance/Position Determination Microscopy (SPDM). SPDM, a variant of localization microscopy, makes use of conventional fluorescent proteins or single standard organic fluorophores in combination with standard (or only slightly modified) specimen preparation conditions; in its actual realization mode, the same laser frequency can be used for both photoswitching and fluorescence read out. Presently, the SPDM method allows us to image nuclear genome organization in individual cells down to few tens of nanometer (nm) of structural resolution, and to perform quantitative analyses of individual small chromatin domains; of the nanoscale distribution of histones, chromatin remodeling proteins, and transcription, splicing and repair related factors. As a biomedical research application, using dual-color SPDM, it became possible to monitor in mouse cardiomyocyte cells quantitatively the effects of ischemia conditions on the chromatin nanostructure (DNA). These novel "molecular optics" approaches open an avenue to study the nuclear landscape directly in individual cells down to the single molecule level and thus to test models of functional genome architecture at unprecedented resolution.

Spectroscopic super-resolution fluorescence cell imaging using ultra-small Ge quantum dots

We demonstrate a spectroscopic imaging based super-resolution approach by separating the overlapping diffraction spots into several detectors during a single scanning period and taking advantage of the size-dependent emission wavelength in nanoparticles. This approach has been tested using off-the-shelf quantum dots (Invitrogen Qdot) and in-house novel ultra-small (~3 nm) Ge QDs. Furthermore, we developed a method-specific Gaussian fitting and maximum likelihood estimation based on a Matlab algorithm for fast QD localisation. This methodology results in a three-fold improvement in the number of localised QDs compared to non-spectroscopic images. With the addition of advanced ultra-small Ge probes, the number can be improved even further, giving at least 1.5 times improvement when compared to Qdots. Using a standard scanning confocal microscope we achieved a data acquisition rate of 200 ms per image frame. This is an improvement on single molecule localisation super-resolution microscopy where repeated image capture limits the imaging speed, and the size of fluorescence probes limits the possible theoretical localisation resolution. We show that our spectral deconvolution approach has a potential to deliver data acquisition rates on the ms scale thus providing super-resolution in live systems.

MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.

Virtual cell imaging: A review on simulation methods employed in image cytometry

The simulations of cells and microscope images thereof have been used to facilitate the development, selection, and validation of image analysis algorithms employed in cytometry as well as for modeling and understanding cell structure and dynamics beyond what is visible in the eyepiece. The simulation approaches vary from simple parametric models of specific cell components-especially shapes of cells and cell nuclei-to learning-based synthesis and multi-stage simulation models for complex scenes that simultaneously visualize multiple object types and incorporate various properties of the imaged objects and laws of image formation. This review covers advances in artificial digital cell generation at scales ranging from particles up to tissue synthesis and microscope image simulation methods, provides examples of the use of simulated images for various purposes ranging from subcellular object detection to cell tracking, and discusses how such simulators have been validated. Finally, the future possibilities and limitations of simulation-based validation are considered. © 2016 International Society for Advancement of Cytometry.

Correction for specimen movement and rotation errors for in-vivo Optical Projection Tomography

The application of optical projection tomography to in-vivo experiments is limited by specimen movement during the acquisition. We present a set of mathematical correction methods applied to the acquired data stacks to correct for movement in both directions of the image plane. These methods have been applied to correct experimental data taken from in-vivo optical projection tomography experiments in Caenorhabditis elegans. Successful reconstructions for both fluorescence and white light (absorption) measurements are shown. Since no difference between movement of the animal and movement of the rotation axis is made, this approach at the same time removes artifacts due to mechanical drifts and errors in the assumed center of rotation.