Roadmap on holography

From its inception holography has proven an extremely productive and attractive area of research. While specific technical applications give rise to 'hot topics', and three-dimensional (3D) visualisation comes in and out of fashion, the core principals involved continue to lead to exciting innovations in a wide range of areas. We humbly submit that it is impossible, in any journal document of this type, to fully reflect current and potential activity; however, our valiant contributors have produced a series of documents that go no small way to neatly capture progress across a wide range of core activities. As editors we have attempted to spread our net wide in order to illustrate the breadth of international activity. In relation to this we believe we have been at least partially successful.This work was supported by Ministerio de Economía, Industria y Competitividad (Spain) under projects FIS2017-82919-R (MINECO/AEI/FEDER, UE) and FIS2015-66570-P (MINECO/FEDER), and by Generalitat Valenciana (Spain) under project PROMETEO II/2015/015

Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition

Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification. Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes. Approach: The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images. Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes. Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo.

The importance of sharing experimental data in neuroscience grows with the amount and complexity of data acquired and various techniques used to obtain and process these data. However, the majority of experimental data, especially from individual studies of regular-sized laboratories never reach wider research community. A graphical user interface (GUI) engine called Neurovascular Network Explorer 2.0 (NNE 2.0) has been created as a tool for simple and low-cost sharing and exploring of vascular imaging data. NNE 2.0 interacts with a database containing optogenetically-evoked dilation/constriction time-courses of individual vessels measured in mice somatosensory cortex in vivo by 2-photon microscopy. NNE 2.0 enables selection and display of the time-courses based on different criteria (subject, branching order, cortical depth, vessel diameter, arteriolar tree) as well as simple mathematical manipulation (e.g. averaging, peak-normalization) and data export. It supports visualization of the vascular network in 3D and enables localization of the individual functional vessel diameter measurements within vascular trees. NNE 2.0, its source code, and the corresponding database are freely downloadable from UCSD Neurovascular Imaging Laboratory website1. The source code can be utilized by the users to explore the associated database or as a template for databasing and sharing their own experimental results provided the appropriate format

Morphology of Apoptotic, necrotic and oncotic cells.

<p><b>A.</b> Characteristic apoptotic, necrotic and oncotic cells in multimodal holographic microscope, simulated DIC (differential interference contrast). 20 × magnification was used in MHM. Annexin V staining for the verification of cell membrane alteration. Red arrow indicates annexin V-positive “advanced” oncotic cell. Apoptotic cells displayed in initial step (left) with the typically round-shaped cells and in advanced step with the formation of apoptotic bodies. <b>B.</b> Scheme of typical apoptotic, necrotic and oncotic cells. Typical characteristics visible by MHM phase image. For a detailed description of the characteristic features of apoptotic, necrotic, and oncotic cells, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121674#pone.0121674.t001" target="_blank">Table 1</a>.</p

Estimation of oncosis progression by multimodal holographic microscopy.

<p>Annexin V (green) and propidium iodide (PI, red) staining. Initial step of oncosis (first row, red arrow) is annexin V−/PI− and thus distinguishable only by native morphology, see typical cytoplasmic bleb in the phase image. This causes false-negativity by flow-cytometry. Second, early oncotic cells feature larger blebs and are annexin V+/PI−. Late oncosis is double positive for staining, with no apparent karyolysis. Advanced oncosis/necrosis transition is typical by double-positive staining and karyolysis.</p

Characteristic apoptotic, necrotic and oncotic cells in transmission electron microscope (TEM).

<p><b>A</b>. Apoptotic cell, overall view, 2800 ×. <b>B</b>. necrotic cell, 2800 ×. <b>C</b>. oncotic cell, 2800 ×, <b>D</b>. Detail of apoptotic cell nucleus, 5600 ×. <b>E</b>. Detail of necrotic cell, 14000 ×. <b>F</b>. Detail of oncotic cell cytoplasm, 11000 × magnification. Red arrow—nuclear fragmentation. Black arrow—rupture of plasmatic membrane. White arrow—karyolysis. Yellow arrow—reticular nucleolus. Dark blue arrow—dilatation of nucleus. Green arrow—dilatation of ER and Golgi. Pink arrow—cytoplasmic bleb. Light blue arrow—chromatin condensation. Violet arrow—formation of cytoplasmic vacuoles. Orange arrow—initial lysis of nucleolus. Brown arrow—mitochondrial swelling.</p

Holographic mode setup in Multimodal holographic microscope is based on the Mach-Zehnder-type interferometer.

<p>The light is divided into two separate optical paths—object arm and interferometer reference arm. Both arms consist of condenser (C), objective (O) and tube lens (TL). In the reference arm, a diffraction grating (DG) is placed. The object beam and the reference beam recombine in the output plane and create interference fringes pattern, which is captured by the camera (D). S—source; CL—collector lens; BS—beam splitter; M—mirror; C—condenser; O-objective; TL—tube lens; DG—diffraction grating; OL—output lens; D—detector.</p