Multispectral lensless digital holographic microscope: imaging MCF-7 and MDA-MB-231 cancer cell cultures

Digital holography is the process where an object’s phase and amplitude information is retrieved from intensity images obtained using a digital camera (e.g. CCD or CMOS sensor). In-line digital holographic techniques offer full use of the recording device’s sampling bandwidth, unlike off-axis holography where object information is not modulated onto carrier fringes. Reconstructed images are obscured by the linear superposition of the unwanted, out of focus, twin images. In addition to this, speckle noise degrades overall quality of the reconstructed images. The speckle effect is a phenomenon of laser sources used in digital holographic systems. Minimizing the effects due to speckle noise, removal of the twin image and using the full sampling bandwidth of the capture device aids overall reconstructed image quality. Such improvements applied to digital holography can benefit applications such as holographic microscopy where the reconstructed images are obscured with twin image information. Overcoming such problems allows greater flexibility in current image processing techniques, which can be applied to segmenting biological cells (e.g. MCF-7 and MDA-MB- 231) to determine their overall cell density and viability. This could potentially be used to distinguish between apoptotic and necrotic cells in large scale mammalian cell processes, currently the system of choice, within the biopharmaceutical industry

Classification of human carcinoma cells using multispectral imagery

In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options. © 2016 SPIE

Multiplexed spectral imaging of 120 different fluorescent labels

This article is distributed under the terms of the Creative Commons public domain dedication. The definitive version was published in PLoS One 11 (2016): e0158495, doi:10.1371/journal.pone.0158495.The number of fluorescent labels that can unambiguously be distinguished in a single image when acquired through band pass filters is severely limited by the spectral overlap of available fluorophores. The recent development of spectral microscopy and the application of linear unmixing algorithms to spectrally recorded image data have allowed simultaneous imaging of fluorophores with highly overlapping spectra. However, the number of distinguishable fluorophores is still limited by the unavoidable decrease in signal to noise ratio when fluorescence signals are fractionated over multiple wavelength bins. Here we present a spectral image analysis algorithm to greatly expand the number of distinguishable objects labeled with binary combinations of fluorophores. Our algorithm utilizes a priori knowledge about labeled specimens and imposes a binary label constraint on the unmixing solution. We have applied our labeling and analysis strategy to identify microbes labeled by fluorescence in situ hybridization and here demonstrate the ability to distinguish 120 differently labeled microbes in a single image.This work was supported by Grant 2007-3- 13 from the Alfred P. Sloan Foundation (to GGB), National Institutes of Health Grant 1RC1-DE020630 from the National Institute of Dental and Craniofacial Research (NIDCR) (to GGB) and by National Institutes of Health Fellowship 1F31-DE019576 from NIDCR (to AMV)

Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Accessible software frameworks for reproducible image analysis of host-pathogen interactions

Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird

Single-side access, isotropic resolution and multispectral 3D photoacoustic imaging with rotate-translate scanning of ultrasonic detector array

Photoacoustic imaging can achieve high-resolution three-dimensional visualization of optical absorbers at penetration depths ~ 1 cm in biological tissues by detecting optically-induced high ultrasound frequencies. Tomographic acquisition with ultrasound linear arrays offers an easy implementation of single-side access, parallelized and high-frequency detection, but usually comes with an image quality impaired by the directionality of the detectors. Indeed, a simple translation of the array perpendicularly to its median imaging plane is often used, but results both in a poor resolution in the translation direction and in strong limited view artifacts. To improve the spatial resolution and the visibility of complex structures while keeping a planar detection geometry, we introduce, in this paper, a novel rotate-translate scanning scheme, and investigate the performance of a scanner implemented at 15 MHz center frequency. The developed system achieved a quasi-isotropic uniform 3D resolution of ~170 um over a cubic volume of side length 8.5 mm, i.e. an improvement in the resolution in the translation direction by almost one order of magnitude. Dual wavelength imaging was also demonstrated with ultrafast wavelength shifting. The validity of our approach was shown in vitro. We discuss the ability to enable in vivo imaging for preclinical and clinical studies.Comment: 43 pages, 5 figure

Studying Pre-formed Fibril Induced α-Synuclein Accumulation in Primary Embryonic Mouse Midbrain Dopamine Neurons

The goal of this protocol is to establish a robust and reproducible model of α-synuclein accumulation in primary dopamine neurons. Combined with immunostaining and unbiased automated image analysis, this model allows for the analysis of the effects of drugs and genetic manipulations on α-synuclein aggregation in neuronal cultures. Primary midbrain cultures provide a reliable source of bona fide embryonic dopamine neurons. In this protocol, the hallmark histopathology of Parkinson’s disease, Lewy bodies (LB), is mimicked by the addition of α-synuclein pre-formed fibrils (PFFs) directly to neuronal culture media. Accumulation of endogenous phosphorylated α-synuclein in the soma of dopamine neurons is detected by immunostaining already at 7 days after the PFF addition. In vitro cell culture conditions are also suitable for the application and evaluation of treatments preventing α-synuclein accumulation, such as small molecule drugs and neurotrophic factors, as well as lentivirus vectors for genetic manipulation (e.g., with CRISPR/Cas9). Culturing the neurons in 96 well plates increases the robustness and power of the experimental setups. At the end of the experiment, the cells are fixed with paraformaldehyde for immunocytochemistry and fluorescence microscopy imaging. Multispectral fluorescence images are obtained via automated microscopy of 96 well plates. These data are quantified (e.g., counting the number of phospho-α-synuclein-containing dopamine neurons per well) with the use of free software that provides a platform for unbiased high-content phenotype analysis. PFF-induced modeling of phosphorylated α-synuclein accumulation in primary dopamine neurons provides a reliable tool to study the underlying mechanisms mediating formation and elimination of α-synuclein inclusions, with the opportunity for high-throughput drug screening and cellular phenotype analysis.Peer reviewe