Kishore Mosaliganti

Specified Neural Progenitors Sort to Form Sharp Domains after Noisy Shh Signaling

Sharply delineated domains of cell types arise in developing tissues under instruction of inductive signal (morphogen) gradients, which specify distinct cell fates at different signal levels. The translation of a morphogen gradient into discrete spatial domains relies on precise signal responses at stable cell positions. However, cells in developing tissues undergoing morphogenesis and proliferation often experience complex movements, which may affect their morphogen exposure, specification, and positioning. How is a clear pattern achieved with cells moving around? Using in toto imaging of the zebrafish neural tube, we analyzed specification patterns and movement trajectories of neural progenitors. We found that specified progenitors of different fates are spatially mixed following heterogeneous Sonic Hedgehog signaling responses. Cell sorting then rearranges them into sharply bordered domains. Ectopically induced motor neuron progenitors also robustly sort to correct locations. Our results reveal that cell sorting acts to correct imprecision of spatial patterning by noisy inductive signals.

ACME: automated cell morphology extractor for comprehensive reconstruction of cell membranes.

The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME).

Interplay of Cell Shape and Division Orientation Promotes Robust Morphogenesis of Developing Epithelia

Epithelial cells acquire functionally important shapes (e.g., squamous, cuboidal, columnar) during development. Here, we combine theory, quantitative imaging, and perturbations to analyze how tissue geometry, cell divisions, and mechanics interact to shape the presumptive enveloping layer (pre-EVL) on the zebrafish embryonic surface. We find that, under geometrical constraints, pre-EVL flattening is regulated by surface cell number changes following differentially oriented cell divisions. The division pattern is, in turn, determined by the cell shape distribution, which forms under geometrical constraints by cell-cell mechanical coupling. An integrated mathematical model of this shape-division feedback loop recapitulates empirical observations. Surprisingly, the model predicts that cell shape is robust to changes of tissue surface area, cell volume, and cell number, which we confirm in vivo. Further simulations and perturbations suggest the parameter linking cell shape and division orientation contributes to epithelial diversity. Together, our work identifies an evolvable design logic that enables robust cell-level regulation of tissue-level development.

Robust 3D Reconstruction and Identification of Dendritic Spines from Optical Microscopy Imaging

In neurobiology, the 3D reconstruction of neurons followed by the identification of dendritic spines is essential for studying neuronal morphology, function and biophysical properties. Most existing methods suffer from problems of low reliability, poor accuracy and require much user interaction. In this paper, we present a method to reconstruct dendrites using a surface representation of the neuron. The skeleton of the dendrite is extracted by a procedure based on the medial geodesic function that is robust and topology preserving, and it is used to accurately identify spines. The sensitivity of the algorithm on the various parameters is explored in detail and the method is shown to be robust.

Registration and 3D visualization of large microscopy images

Inactivation of the retinoblastoma gene in mouse embryos causes tissue infiltrations into critical sections of the placenta, which has been shown to affect fetal survivability. Our collaborators in cancer genetics are extremely interested in examining the three dimensional nature of these infiltrations given a stack of two dimensional light microscopy images. Three sets of wildtype and mutant placentas was sectioned serially and digitized using a commercial light microscopy scanner. Each individual placenta dataset consisted of approximately 1000 images totaling 700 GB in size, which were registered into a volumetric dataset using National Library of Medicines (NIH/NLM) Insight Segmentation and Registration Toolkit (ITK). This paper describes our method for image registration to aid in volume visualization of tissue level intermixing for both wildtype and Rb- specimens. The registration process faces many challenges arising from the large image sizes, damages during sectioning, staining gradients both within and across sections, and background noise. These issues limit the direct application of standard registration techniques due to frequent convergence to local solutions. In this work, we develop a mixture of automated and semi-automated enhancements with ground-truth validation for the mutual information-based registration algorithm. Our final volume renderings clearly show tissue intermixing differences between both wildtype and Rb- specimens which are not obvious prior to registration.

Membrane dynamics of dividing cells imaged by lattice light-sheet microscopy

Lattice light-sheet microscopy is used to examine two problems in membrane dynamics—molecular events in clathrin-coated pit formation and changes in cell shape during cell division. This methodology sets a new standard for imaging membrane dynamics in single cells and multicellular assemblies.

Reconstruction of Cellular Biological Structures from Optical Microscopy Data

Developments in optical microscopy imaging have generated large high-resolution data sets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer at cellular and subcellular levels. The work reported here demonstrates that a suitable methodology can be conceived that isolates modality-dependent effects from the larger segmentation task and that 3D reconstructions can be cognizant of shapes as evident in the available 2D planar images. In the current realization, a method based on active geodesic contours is first deployed to counter the ambiguity that exists in separating overlapping cells on the image plane. Later, another segmentation effort based on a variant of Voronoi tessellations improves the delineation of the cell boundaries using a Bayesian formulation. In the next stage, the cells are interpolated across the third dimension thereby mitigating the poor structural correlation that exists in that dimension. We deploy our methods on three separate data sets obtained from light, confocal, and phase-contrast microscopy and validate the results appropriately.

Tensor classification of N-point correlation function features for histology tissue segmentation.

In this paper, we utilize the N-point correlation functions (N-pcfs) to construct an appropriate feature space for achieving tissue segmentation in histology-stained microscopic images. The N-pcfs estimate microstructural constituent packing densities and their spatial distribution in a tissue sample. We represent the multi-phase properties estimated by the N-pcfs in a tensor structure. Using a variant of higher-order singular value decomposition (HOSVD) algorithm, we realize a robust classifier that provides a multi-linear description of the tensor feature space. Validated results of the segmentation are presented in a case-study that focuses on understanding the genetic phenotyping differences in mouse placentae.

Detection of Spatially Correlated Objects in 3D Images Using Appearance Models and Coupled Active Contours

We consider the problem of segmenting 3D images that contain a dense collection of spatially correlated objects, such as fluorescent labeled cells in tissue. Our approach involves an initial modeling phase followed by a data-fitting segmentation phase. In the first phase, cell shape (membrane bound) is modeled implicitly using a parametric distribution of correlation function estimates. The nucleus is modeled for its shape as well as image intensity distribution inspired from the physics of its image formation. In the second phase, we solve the segmentation problem using a variational level-set strategy with coupled active contours to minimize a novel energy functional. We demonstrate the utility of our approach on multispectral fluorescence microscopy images.

Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms

Continuing the resolution revolution The living cell contains dynamic, spatially complex subassemblies that are sensitive to external perturbations. To minimize such perturbations, cells should be imaged in their native multicellular environments, under as gentle illumination as possible. However, achieving the spatiotemporal resolution needed to follow three-dimensional subcellular processes in detail under these conditions is challenging: Sample-induced aberrations degrade resolution and sensitivity, and high resolution usually requires intense excitation. Liu et al. combined noninvasive lattice light-sheet microscopy with aberration-correcting adaptive optics to study a variety of delicate subcellular events in vivo, including organelle remodeling during mitosis and growth cone dynamics during spinal cord development. Science, this issue p. eaaq1392 Adaptive optical lattice light-sheet microscopy permits delicate 3D subcellular processes to be viewed natively in vivo. INTRODUCTION Organisms live by means of the complex, dynamic, three-dimensional (3D) interplay between millions of components, from the molecular to the multicellular. Visualizing this complexity in its native form requires imaging at high resolution in space and time anywhere within the organism itself, because only there are all the environmental factors that regulate its physiology present. However, the optical heterogeneity of multicellular systems leads to aberrations that quickly compromise resolution, signal, and contrast with increasing imaging depth. Furthermore, even in the absence of aberrations, high resolution and fast imaging are usually accompanied by intense illumination, which can perturb delicate subcellular processes or even introduce permanent phototoxic effects. RATIONALE We combined two imaging technologies to address these problems. The first, lattice light-sheet microscopy (LLSM), rapidly and repeatedly sweeps an ultrathin sheet of light through a volume of interest while acquiring a series of images, building a high-resolution 3D movie of the dynamics within. The confinement of the illumination to a thin plane insures that regions outside the volume remain unexposed, while the parallel collection of fluorescence from across the plane permits low, less perturbative intensities to be used. The second technology, adaptive optics (AO), measures sample-induced distortions to the image of a fluorescent “guide star” created within the volume—distortions that also affect the acquired light-sheet images—and compensates for these by changing the shape of a mirror to create an equal but opposite distortion. RESULTS We applied AO-LLSM to study a variety of 3D subcellular processes in vivo over a broad range of length scales, from the nanoscale diffusion of clathrin-coated pits (CCPs) to axon-guided motility across 200 μm of the developing zebrafish spinal cord. Clear delineation of cell membranes allowed us to computationally isolate and individually study any desired cell within the crowded multicellular environment of the intact organism. By doing so, we could compare specific processes across different cell types, such as rates of CCP internalization in muscle fibers and brain cells, organelle remodeling during cell division in the developing brain and eye, and motility mechanisms used by immune cells and metastatic breast cancer cells. Although most examples were taken from zebrafish embryos, we also demonstrated AO-LLSM in a human stem cell–derived organoid, a Caenorhabditis elegans nematode, and Arabidopsis thaliana leaves. CONCLUSION AO-LLSM takes high-resolution live-cell imaging of subcellular processes from the confines of the coverslip to the more physiologically relevant 3D environment within whole transparent organisms. This creates new opportunities to study the phenotypic diversity of intracellular dynamics, extracellular communication, and collective cell behavior across different cell types, organisms, and developmental stages. High-resolution in vivo cell biology. AO-LLSM permits the study of 3D subcellular processes in their native multicellular environments at high spatiotemporal resolution, including (clockwise from upper left) growth of spinal cord axons; cancer cell metastasis; collective cellular motion; endocytosis; microtubule displacements; immune cell migration; and (center) organelle dynamics. True physiological imaging of subcellular dynamics requires studying cells within their parent organisms, where all the environmental cues that drive gene expression, and hence the phenotypes that we actually observe, are present. A complete understanding also requires volumetric imaging of the cell and its surroundings at high spatiotemporal resolution, without inducing undue stress on either. We combined lattice light-sheet microscopy with adaptive optics to achieve, across large multicellular volumes, noninvasive aberration-free imaging of subcellular processes, including endocytosis, organelle remodeling during mitosis, and the migration of axons, immune cells, and metastatic cancer cells in vivo. The technology reveals the phenotypic diversity within cells across different organisms and developmental stages and may offer insights into how cells harness their intrinsic variability to adapt to different physiological environments.