scholarly journals Deciphering tumour tissue organization by 3D electron microscopy and machine learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Baudouin Denis de Senneville ◽  
Fatma Zohra Khoubai ◽  
Marc Bevilacqua ◽  
Alexandre Labedade ◽  
Kathleen Flosseau ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Spatial Organization ◽  
Hot Spot ◽  
Bile Canaliculus ◽  
Tumour Cells ◽  
Tumour Tissue ◽  
Blood Capillary ◽  
Tissue Organization ◽  
3D Electron Microscopy

AbstractDespite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organization of patient-derived xenograft tissues generated from hepatoblastoma, the most frequent childhood liver tumour, by serial block-face scanning electron microscopy using an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentations, mathematics and infographics. By digitally reconstituting an entire hepatoblastoma sample with a blood capillary, a bile canaliculus-like structure, hundreds of tumour cells and their main organelles (e.g. cytoplasm, nucleus, mitochondria), we report unique 3D ultrastructural data about the organization of tumour tissue. We found that the size of hepatoblastoma cells correlates with the size of their nucleus, cytoplasm and mitochondrial mass. We also found anatomical connections between the blood capillary and the planar alignment and size of tumour cells in their 3D milieu. Finally, a set of tumour cells polarized in the direction of a hot spot corresponding to a bile canaliculus-like structure. In conclusion, this pilot study allowed the identification of bioarchitectural parameters that shape the internal and spatial organization of tumours, thus paving the way for future investigations in the emerging onconanotomy field.

scholarly journals Deciphering Tumour Tissue Organization by 3D Electron Microscopy and machine learning

2021 ◽  
Author(s):  
Baudouin Denis de Senneville ◽  
Fatma Zohra Khoubai ◽  
Marc Bevilacqua ◽  
Alexandre Labedade ◽  
Kathleen Flosseau ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Spatial Organization ◽  
Hot Spot ◽  
Bile Canaliculus ◽  
Tumour Cells ◽  
Blood Capillary ◽  
Tissue Organization ◽  
3D Electron Microscopy ◽  
Block Face

Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organization of patient-derived xenograft tissues generated from hepatoblastoma, the most frequent childhood liver tumour, by serial block-face scanning electron microscopy using an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentations, mathematics and infographics. By digitally reconstituting an entire hepatoblastoma sample with a blood capillary, a bile canaliculus-like structure, hundreds of tumour cells and their main organelles (e.g. cytoplasm, nucleus, mitochondria), we report unique 3D ultrastructural data about the organization of tumoral tissue. We found that the size of hepatoblastoma cells correlates with the size of their nucleus, cytoplasm and mitochondrial mass. We also discovered that the blood capillary controls the planar alignment and size of tumour cells in their 3D milieu. Finally, a set of tumour cells polarized in the direction of a hot spot corresponding to a bile canaliculus-like structure. In conclusion, this pilot study allowed the identification of bioarchitectural parameters that shape the internal and spatial organization of tumours, thus paving the way for new investigations in an emerging field that we call onconanotomy.

scholarly journals Protocol for preparation of heterogeneous biological samples for 3D electron microscopy: a case study for insects

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexey A. Polilov ◽  
Anastasia A. Makarova ◽  
Song Pang ◽  
C. Shan Xu ◽  
Harald Hess
Keyword(s):  
Electron Microscopy ◽  
Biological Samples ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Three Dimensional ◽  
Entire Volume ◽  
Simple Protocol ◽  
3D Electron Microscopy ◽  
3D Em

AbstractModern morphological and structural studies are coming to a new level by incorporating the latest methods of three-dimensional electron microscopy (3D-EM). One of the key problems for the wide usage of these methods is posed by difficulties with sample preparation, since the methods work poorly with heterogeneous (consisting of tissues different in structure and in chemical composition) samples and require expensive equipment and usually much time. We have developed a simple protocol allows preparing heterogeneous biological samples suitable for 3D-EM in a laboratory that has a standard supply of equipment and reagents for electron microscopy. This protocol, combined with focused ion-beam scanning electron microscopy, makes it possible to study 3D ultrastructure of complex biological samples, e.g., whole insect heads, over their entire volume at the cellular and subcellular levels. The protocol provides new opportunities for many areas of study, including connectomics.

scholarly journals Prediction of Hourly Effect of Land Use on Crime

2018 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Irina Matijosaitiene ◽  
Peng Zhao ◽  
Sylvain Jaume ◽  
Joseph Gilkey Jr
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Logistic Regression ◽  
Hot Spots ◽  
Urban Areas ◽  
Prediction Models ◽  
Hot Spot ◽  
Time Range ◽  
Land Uses ◽  
Police Departments

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.

scholarly journals Patterns of organelle ontogeny through a cell cycle revealed by whole-cell reconstructions using 3D electron microscopy

Development ◽  
2017 ◽  
Vol 144 (4) ◽  
pp. e1.2-e1.2
Author(s):  
Louise Hughes ◽  
Samantha Borrett ◽  
Katie Towers ◽  
Tobias Starborg ◽  
Sue Vaughan
Keyword(s):  
Electron Microscopy ◽  
Cell Cycle ◽  
Whole Cell ◽  
3D Electron Microscopy ◽  
A Cell ◽  
3D Electron

scholarly journals Quantitative method for estimating stain density in electron microscopy of conventionally prepared biological specimens

2019 ◽  
Author(s):  
Andrea Fera ◽  
Qianping He ◽  
Guofeng Zhang ◽  
Richard D. Leapman
Keyword(s):  
Electron Microscopy ◽  
Focused Ion Beam ◽  
Electron Tomography ◽  
Blood Platelets ◽  
Ion Beam ◽  
Simple Expression ◽  
Heavy Atoms ◽  
3D Electron Microscopy ◽  
Block Face ◽  
Microscopy Techniques

SummaryStain density is an important parameter for optimizing the quality of ultrastructural data obtained from several types of 3D electron microscopy techniques, including serial block-face electron microscopy (SBEM), and focused ion beam scanning electron microscopy (FIB-SEM). Here, we show how some straightforward measurements in the TEM can be used to determine the stain density based on a simple expression that we derive. Numbers of stain atoms per unit volume are determined from the measured ratio of the bright-field intensities from regions of the specimen that contain both pure embedding material and the embedded biological structures of interest. The determination only requires knowledge of the section thickness, which can either be estimated from the microtome setting, or from low-dose electron tomography, and the elastic scattering cross section for the heavy atoms used to stain the specimen. The method is tested on specimens of embedded blood platelets, brain tissue, and liver tissue.

scholarly journals Work Flows for Cellular Epidemiology, From Conception to Translation

2019 ◽  
Author(s):  
Anton D. Nathanson ◽  
Lucy Ngo ◽  
Tomasz Garbowski ◽  
Abhilash Srikantha ◽  
Christian Wojek ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Image Resolution ◽  
Machine Learning Algorithms ◽  
Human Anatomy ◽  
Test Case ◽  
Health State ◽  
Learning Approaches ◽  
Preparation Methods ◽  
Trade Offs

AbstractChanges in cell connectivity and morphology, observed and measured using microscopy, implicate a cellular basis of degenerative disease in tissues as diverse as bone, kidney and brain. To date, limitations inherent to sampling (biopsy sites) and/or microscopy (trade-offs between regions of interest and image resolution) have prevented early identification of cellular changes in specimen sizes of diagnostic relevance for human anatomy and physiology. This manuscript describes work flows for human tissue-based cell epidemiology studies. Using recently published sample preparation methods, developed and validated to maximize imaging quality, the largest-to-date scanning electron microscopy map was created showing cellular connections in the femoral neck of a human hip. The map, from a patient undergoing hip replacement, comprises an 11 TB dataset including over 7 million electron microscopy images. This map served as a test case to implement machine learning algorithms for automated detection of cells and identification of their health state. The test case showed a significant link between cell connectivity and health state in osteocytes of the human femur. Combining new, rapid throughput electron microscopy methods with machine learning approaches provides a basis for assessment of cell population health at nanoscopic resolution and in mesoscopic tissue and organ samples. This sets a path for next generation cellular epidemiology, tracking outbreaks of disease in populations of cells that inhabit tissues and organs within individuals.

scholarly journals Three-dimensional electron microscopy of ribosomal chromatin in two higher plants: a cytochemical, immunocytochemical, and in situ hybridization approach.

1991 ◽  
Vol 39 (11) ◽  
pp. 1495-1506 ◽  
Author(s):  
P M Motte ◽  
R Loppes ◽  
M Menager ◽  
R Deltour
Keyword(s):  
Electron Microscopy ◽  
In Situ Hybridization ◽  
Spatial Organization ◽  
Higher Plants ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Thin Sections ◽  
Cytoplasmic Dna ◽  
Immunocytochemical Techniques

We report the 3-D arrangement of DNA within the nucleolar subcomponents from two evolutionary distant higher plants, Zea mays and Sinapis alba. These species are particularly convenient to study the spatial organization of plant intranucleolar DNA, since their nucleoli have been previously reconstructed in 3-D from serial ultra-thin sections. We used the osmium ammine-B complex (a specific DNA stain) on thick sections of Lowicryl-embedded root fragments. Immunocytochemical techniques using anti-DNA antibodies and rDNA/rDNA in situ hybridization were also applied on ultra-thin sections. We showed on tilted images that the OA-B stains DNA throughout the whole thickness of the section. In addition, very low quantities of cytoplasmic DNA were stained by this complex, which is now the best DNA stain used in electron microscopy. Within the nucleoli the DNA was localized in the fibrillar centers, where large clumps of dense chromatin were also visible. In the two plant species intranucleolar chromatin forms a complex network with strands partially linked to chromosomal nucleolar-organizing regions identified by in situ hybridization. This study describes for the first time the spatial arrangement of the intranucleolar chromatin in nucleoli of higher plants using high-resolution techniques.

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