scholarly journals BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data

2020 ◽  
Author(s):  
Christian Tischer ◽  
Ashis Ravindran ◽  
Sabine Reither ◽  
Rainer Pepperkok ◽  
Nils Norlin
Keyword(s):  
Electron Microscopy ◽  
Open Source ◽  
Imaging Techniques ◽  
Image Data ◽  
Light Sheet ◽  
Sensor Technology ◽  
Time Points ◽  
Light Sheet Microscopy ◽  
Link Type ◽  
Image Datasets

SummaryModern bioimaging and related areas such as sensor technology has seen tremendous development the last years allowing several contemporary imaging techniques, electron microscopy (EM) and light sheet microscopy in particular, to generate datasets frequently reaching the size of several terabytes (TB). As a consequence, even seemingly simple data operations such as cropping, chromatic- and drift-corrections and even visualisation, poses challenges when applied to thousands of time points or tiles. To address this we developed BigDataProcessor2 – a Fiji plugin facilitating processing workflows for TB sized image datasets.Availability and implementationBigDataProcessor2 is available as a Fiji plugin via the BigDataProcessor update site. The application is implemented in Java and the code is publicly available on GitHub (https://github.com/bigdataprocessor/bigdataprocessor2)[email protected], [email protected]

scholarly journals BigDataProcessor2: a free and open-source Fiji plugin for inspection and processing of TB sized image data

2021 ◽  
Author(s):  
Christian Tischer ◽  
Ashis Ravindran ◽  
Sabine Reither ◽  
Nicolas Chiaruttini ◽  
Rainer Pepperkok ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Open Source ◽  
Imaging Techniques ◽  
Image Data ◽  
Light Sheet ◽  
Supplementary Information ◽  
Sensor Technology ◽  
Supplementary Data ◽  
Time Points ◽  
Light Sheet Microscopy

Abstract Summary Modern bioimaging and related areas such as sensor technology have undergone tremendous development over the last few years. As a result, contemporary imaging techniques, particularly electron microscopy (EM) and light sheet microscopy, can frequently generate datasets attaining sizes of several terabytes (TB). As a consequence, even seemingly simple data operations such as cropping, chromatic- and drift-corrections and even visualisation, poses challenges when applied to thousands of time points or tiles. To address this we developed BigDataProcessor2—a Fiji plugin facilitating processing workflows for TB sized image datasets. Availability and implementation BigDataProcessor2 is available as a Fiji plugin via the BigDataProcessor update site. The application is implemented in Java and the code is publicly available on GitHub (https://github.com/bigdataprocessor/bigdataprocessor2). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Tracking cells in epithelial acini by light sheet microscopy reveals proximity effects in breast cancer initiation

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Ashna Alladin ◽  
Lucas Chaible ◽  
Lucia Garcia del Valle ◽  
Reither Sabine ◽  
Monika Loeschinger ◽  
...  
Keyword(s):  
Single Cells ◽  
Image Data ◽  
Integrated Approach ◽  
Light Sheet ◽  
Mammary Epithelial ◽  
Proximity Effects ◽  
Transformed Cells ◽  
Light Sheet Microscopy ◽  
Primary Mouse ◽  
Longitudinal Imaging

Cancer clone evolution takes place within tissue ecosystem habitats. But, how exactly tumors arise from a few malignant cells within an intact epithelium is a central, yet unanswered question. This is mainly due to the inaccessibility of this process to longitudinal imaging together with a lack of systems that model the progression of a fraction of transformed cells within a tissue. Here, we developed a new methodology based on primary mouse mammary epithelial acini, where oncogenes can be switched on in single cells within an otherwise normal epithelial cell layer. We combine this stochastic breast tumor induction model with inverted light-sheet imaging to study single-cell behavior for up to four days and analyze cell fates utilizing a newly developed image-data analysis workflow. The power of this integrated approach is illustrated by us finding that small local clusters of transformed cells form tumors while isolated transformed cells do not.

ClearVolume: open-source live 3D visualization for light-sheet microscopy

2015 ◽  
Vol 12 (6) ◽  
pp. 480-481 ◽  
Author(s):  
Loic A Royer ◽  
Martin Weigert ◽  
Ulrik Günther ◽  
Nicola Maghelli ◽  
Florian Jug ◽  
...  
Keyword(s):  
Open Source ◽  
3D Visualization ◽  
Light Sheet ◽  
Light Sheet Microscopy

scholarly journals SynapseJ: an automated, synapse identification macro for ImageJ

2021 ◽  
Author(s):  
Juan Felipe Moreno Manrique ◽  
Parker R. Voit ◽  
Kathryn E. Windsor ◽  
Aamuktha R. Karla ◽  
Sierra R. Rodriguez ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Open Source ◽  
Fluorescent Protein ◽  
Imaging Techniques ◽  
Yellow Fluorescent Protein ◽  
Synaptic Proteins ◽  
Scaffolding Protein ◽  
Substantia Nigra Pars Compacta ◽  
Distinct Advantage ◽  
Specific Cell

While electron microscopy represents the gold standard for detection of synapses, a number of limitations prevent its broad applicability. A key method for detecting synapses is immunostaining for markers of pre- and post-synaptic proteins, which can infer a synapse based upon the apposition of the two markers. While immunostaining and imaging techniques have improved to allow for identification of synapses in tissue, analysis and identification of these appositions are not facile, and there has been a lack of tools to accurately identify these appositions. Here, we delineate a macro that uses open-source and freely available ImageJ or FIJI for analysis of multichannel, z-stack confocal images. With use of a high magnification with a high NA objective, we outline two methods to identify puncta in either sparsely or densely labeled images. Puncta from each channel are used to eliminate non-apposed puncta and are subsequently linked with their cognate from the other channel. These methods are applied to analysis of a presynaptic marker, bassoon, with two different postsynaptic markers, gephyrin and N-methyl-d-aspartate (NMDA) receptor subunit 1 (NR1). Using gephyrin as an inhibitory, postsynaptic scaffolding protein, we identify inhibitory synapses in basolateral amygdala, central amygdala, arcuate and the ventromedial hypothalamus. Systematic variation of the settings identify the parameters most critical for this analysis. Identification of specifically overlapping puncta allows for correlation of morphometry data between each channel. Finally, we extend the analysis to only examine puncta overlapping with a cytoplasmic marker of specific cell types, a distinct advantage beyond electron microscopy. Bassoon puncta are restricted to virally transduced, pedunculopontine tegmental neuron (PPN) axons expressing yellow fluorescent protein. NR1 puncta are restricted to tyrosine hydroxylase labeled dopaminergic neurons of the substantia nigra pars compacta (SNc). The macro identifies bassoon-NR1 overlap throughout the image, or those only restricted to the PPN-SNc connections. Thus, we have extended the available analysis tools that can be used to study synapses in situ. Our analysis code is freely available and open-source allowing for further innovation. 

scholarly journals Imaging mitotic processes in three dimensions with lattice light-sheet microscopy

2021 ◽  
Author(s):  
Yuko Mimori-Kiyosue
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Image Data ◽  
Light Sheet ◽  
Three Dimensions ◽  
Cell Level ◽  
Spatiotemporal Resolution ◽  
New Era ◽  
Light Sheet Microscopy ◽  
Three Dimensional Space

AbstractThere are few technologies that can capture mitotic processes occurring in three-dimensional space with the desired spatiotemporal resolution. Due to such technical limitations, our understanding of mitosis, which has been studied since the early 1880s, is still incomplete with regard to mitotic processes and their regulatory mechanisms at a molecular level. A recently developed high-resolution type of light-sheet microscopy, lattice light-sheet microscopy (LLSM), has achieved unprecedented spatiotemporal resolution scans of intracellular spaces at the whole-cell level. This technology enables experiments that were not possible before (e.g., tracking of growth of every spindle microtubule end and discrimination of individual chromosomes in living cells), thus providing a new avenue for the analysis of mitotic processes. Herein, principles of LLSM technology are introduced, as well as experimental techniques that became possible with LLSM. In addition, issues remaining to be solved for use of this technology in mitosis research, big image data problems, are presented to help guide mitosis research into a new era.

scholarly journals Imaging erythrocyte sedimentation in whole blood

2021 ◽  
Author(s):  
Alexis Darras ◽  
Hans Georg Breunig ◽  
Thomas John ◽  
Renping Zhao ◽  
Johannes Koch ◽  
...  
Keyword(s):  
Light Transmission ◽  
Imaging Techniques ◽  
Colloidal Suspensions ◽  
Dynamic Structure ◽  
Light Sheet ◽  
Diagnostic Tools ◽  
Conventional View ◽  
Light Sheet Microscopy ◽  
Erythrocyte Sedimentation ◽  
Medical Diagnostic

AbstractThe erythrocyte sedimentation rate (ESR) is one of the oldest medical diagnostic tools. However, currently there is some debate on the structure formed by the cells during the sedimentation process. While the conventional view is that erythrocytes sediment as separate aggregates, others have suggested that they form a percolating gel, similar to other colloidal suspensions. A direct probing of the structures formed by erythrocytes in blood at stasis is then required to settle these discrepancies. Here, we report observations performed with three different optical imaging techniques: direct light transmission through thin samples, two-photon microscopy and light-sheet microscopy. All techniques revealed a dynamic structure of a channeling gel but with differences in the resolved details. A quantitative analysis of the erythrocyte related processes and interactions during the sedimentation need a further refinement of the experimental set-ups.

scholarly journals Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

2019 ◽  
Author(s):  
Erik C. Johnson ◽  
Miller Wilt ◽  
Luis M. Rodriguez ◽  
Raphael Norman-Tenazas ◽  
Corban Rivera ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Open Source ◽  
Data Storage ◽  
Large Scale ◽  
Imaging Techniques ◽  
Scientific Discovery ◽  
Imaging Data ◽  
Considerable Difficulty ◽  
X Ray ◽  
The Brain

ABSTRACTEmerging neuroimaging datasets (collected through modalities such as Electron Microscopy, Calcium Imaging, or X-ray Microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational expertise or resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. We developed an ecosystem of neuroimaging data analysis pipelines that utilize open source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, that connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets, but may be applied to similar problems in other domains.

scholarly journals Multimodal Magnetic Resonance Histology and Light Sheet Imaging for Quantitative Neurogenetics of the Mouse

2021 ◽  
Author(s):  
G. Allan Johnson ◽  
Gary Cofer ◽  
James Cook ◽  
James Gee ◽  
Adam Hall ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Mouse Brain ◽  
Angular Resolution ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Light Sheet ◽  
Whole Brain ◽  
Light Sheet Microscopy

Paul Lauterbur closed his seminal paper on MRI with the statement that "zeugmatographic (imaging) techniques should find many useful applications in studies of the internal structures, states and composition of microscopic objects" {Lauterbur, 1973 #967}. Magnetic resonance microscopy was subsequently demonstrated in 1986 by three groups{Aguayo, 1986 #968}{Eccles, 1986 #969}{Johnson, 1986 #970}. The application of MRI to the study of tissue structure, i.e. magnetic resonance histology (MRH) was suggested in 1993 {Johnson, 1993 #957}. MRH, while based on the same physical principals as MRI is something fundamentally different than the clinical exams which are typically limited to voxel dimensions of ~ 1 mm3. Preclinical imaging systems can acquire images with voxels ~ 1000 times smaller. The MR histology images presented here have been acquired at yet another factor of 1000 increase in spatial resolution. Figure S1 in the supplement shows a comparison of a state-of-the-art fractional anisotropy images of a C57 mouse brain in vivo @ 150 um resolution (voxel volume of 3.3 x10-3 mm3) with the atlas we have generated for this work at 15 um spatial resolution (voxel volume of 3.3 x 10-6 mm3). In previous work, we have demonstrated the utility of MR histology in neurogenetics at spatial/angular resolution of 45 um /46 angles {Wang N, 2020 #972}. At this spatial/angular resolution it is possible to map whole brain connectivity with high correspondence to retroviral tracers {Calabrese, 2015 #895}. But the MRH derived connectomes can be derived in less than a day where the retroviral tracer studies require months/years {Oh, 2014 #971}. The resolution index (angular samples/voxel volume) for this previous work was >500,000 {Johnson, 2018 #894}. Figure S2 shows a comparison between that previous work and the new atlas presented in this paper with a resolution index of 32 million. Light sheet microscopy (LSM) has undergone similar rapid evolution over the last 20 years. The invention of tissue clearing, advances in immuno histochemistry and development of selective plane illumination microscopy (SPIM) now make it possible to acquire whole mouse brain images at submicron spatial resolution with a vast array of cell specific markers{Ueda, 2020 #974}{Park, 2018 #953}{Murray, 2015 #952}{Gao, 2014 #973}. And these advantages can be realized in scan times of < 6hrs. The major limitation from these studies is the distortion in the tissue from dissection from the cranium, swelling from clearing and staining, and tissue damage from handling. We report here the merger of these two methods: 1. MRH with the brain in the skull to provide accurate geometry, cytoarchitectural measures using scalar imaging metrics and whole brain connectivity at 15 um isotropic spatial resolution with super resolution track density images @ 5 um isotropic resolution; 2. whole brain multichannel LSM @ 1.8x1.8x4.0 um; 3. a big image data infrastructure that enables label mapping from the atlas to the MR image, geometric correction to the light sheet data, label mapping to the light sheet volumes and quantitative extraction of regional cell density. These methods make it possible to generate a comprehensive collection of image derived phenotypes (IDP) of cells and circuits covering the whole mouse brain with throughput that can be scaled for quantitative neurogenetics.

scholarly journals Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Erik C Johnson ◽  
Miller Wilt ◽  
Luis M Rodriguez ◽  
Raphael Norman-Tenazas ◽  
Corban Rivera ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Open Source ◽  
Data Storage ◽  
Large Scale ◽  
Imaging Techniques ◽  
Scientific Discovery ◽  
Imaging Data ◽  
Considerable Difficulty ◽  
X Ray ◽  
The Brain

Abstract Background Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. Results We developed an ecosystem of neuroimaging data analysis pipelines that use open-source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Conclusions Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets but may be applied to similar problems in other domains.

scholarly journals Microfilaria-dependent thoracic pathology associated with eosinophilic and fibrotic polyps in filaria-infected rodents

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Frédéric Fercoq ◽  
Estelle Remion ◽  
Nathaly Vallarino-Lhermitte ◽  
Joy Alonso ◽  
Lisy Raveendran ◽  
...  
Keyword(s):  
Imaging Techniques ◽  
Parasite Load ◽  
Light Sheet ◽  
Meriones Unguiculatus ◽  
Two Photon ◽  
Light Sheet Microscopy ◽  
Pulmonary Manifestations ◽  
Therapeutic Molecules ◽  
Specific Factors ◽  
Gravid Females

Abstract Background Pulmonary manifestations are regularly reported in both human and animal filariasis. In human filariasis, the main known lung manifestations are the tropical pulmonary eosinophilia syndrome. Its duration and severity are correlated with the presence of microfilariae. Litomosoides sigmodontis is a filarial parasite residing in the pleural cavity of rodents. This model is widely used to understand the immune mechanisms that are established during infection and for the screening of therapeutic molecules. Some pulmonary manifestations during the patent phase of infection with L. sigmodontis have been described in different rodent hosts more or less permissive to infection. Methods Here, the permissive Mongolian gerbil (Meriones unguiculatus) was infected with L. sigmodontis. Prevalence and density of microfilariae and adult parasites were evaluated. Lungs were analyzed for pathological signatures using immunohistochemistry and 3D imaging techniques (two-photon and light sheet microscopy). Results Microfilaremia in gerbils was correlated with parasite load, as amicrofilaremic individuals had fewer parasites in their pleural cavities. Fibrotic polypoid structures were observed on both pleurae of infected gerbils. Polyps were of variable size and developed from the visceral mesothelium over the entire pleura. The larger polyps were vascularized and strongly infiltrated by immune cells such as eosinophils, macrophages or lymphocytes. The formation of these structures was induced by the presence of adult filariae since small and rare polyps were observed before patency, but they were exacerbated by the presence of gravid females and microfilariae. Conclusions Altogether, these data emphasize the role of host-specific factors in the pathogenesis of filarial infections.

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