Online Brain Tissue Classification in Multiple Sclerosis using a Scanner-integrated Image Analysis Pipeline

Objectives: To investigate the extent of synaptic loss, and the contribution of gray matter (GM) inflammation and demyelination to synaptic loss, in multiple sclerosis (MS) brain tissue. Methods: This study was performed on two different post-mortem series of MS and control brains, including deep GM and cortical GM. MS brain samples had been specifically selected for the presence of active demyelinating GM lesions. Over 1,000,000 individual synapses were identified and counted using confocal microscopy, and further characterized as glutamatergic/GABAergic. Synaptic counts were also correlated with neuronal/axonal loss. Results: Important synaptic loss was observed in active demyelinating GM lesions (−58.9%), while in chronic inactive GM lesions, synaptic density was only mildly reduced compared to adjacent non-lesional gray matter (NLGM) (−12.6%). Synaptic loss equally affected glutamatergic and GABAergic synapses. Diffuse synaptic loss was observed in MS NLGM compared to control GM (−21.2% overall). Conclusion: This study provides evidence, in MS brain tissue, of acute synaptic damage/loss during active GM inflammatory demyelination and of synaptic reorganization in chronically demyelinated GM, affecting equally glutamatergic and GABAergic synapses. Furthermore, this study provides a strong indication of widespread synaptic loss in MS NLGM also independently from focal GM demyelination.

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Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field

Plants ◽

10.3390/plants10050840 ◽

2021 ◽

Vol 10 (5) ◽

pp. 840

Author(s):

Willem Q. M. van de Koot ◽

Larissa J. J. van Vliet ◽

Weilun Chen ◽

John H. Doonan ◽

Candida Nibau

Keyword(s):

Image Analysis ◽

Organic Matter ◽

Plant Density ◽

Important Determinant ◽

Carbon Sink ◽

High Accuracy ◽

Analysis Pipeline ◽

Colony Density ◽

Water Table Management ◽

And Performance

Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.

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Machine learning-based classification of mitochondrial morphology in primary neurons and brain

Scientific Reports ◽

10.1038/s41598-021-84528-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Garrett M. Fogo ◽

Anthony R. Anzell ◽

Kathleen J. Maheras ◽

Sarita Raghunayakula ◽

Joseph M. Wider ◽

...

Keyword(s):

Image Analysis ◽

Cortical Neurons ◽

Mitochondrial Dynamics ◽

Automated Image Analysis ◽

Mitochondrial Morphology ◽

Analysis Pipeline ◽

Genetic Ablation ◽

Block Face

AbstractThe mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeostatic mitochondrial profile can change morphologic distribution in response to various stressors. Therefore, it is imperative to develop a method that robustly measures mitochondrial morphology with high accuracy. Here, we developed a semi-automated image analysis pipeline for the quantitation of mitochondrial morphology for both in vitro and in vivo applications. The image analysis pipeline was generated and validated utilizing images of primary cortical neurons from transgenic mice, allowing genetic ablation of key components of mitochondrial dynamics. This analysis pipeline was further extended to evaluate mitochondrial morphology in vivo through immunolabeling of brain sections as well as serial block-face scanning electron microscopy. These data demonstrate a highly specific and sensitive method that accurately classifies distinct physiological and pathological mitochondrial morphologies. Furthermore, this workflow employs the use of readily available, free open-source software designed for high throughput image processing, segmentation, and analysis that is customizable to various biological models.

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