Automatic Detection and Neurotransmitter Prediction of Synapses in Electron Microscopy

This paper presents a deep-learning based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) EM images. Identifying synapses from electron microscopy (EM) images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse detection and classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Activation maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables prediction of neurotransmitter types for neurons in Ciona which were previously unknown. The prediction model with code is available on Github.

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Use of human autoantibodies and immunoelectron microscopy to study structure and function of the nucleolus and nuclear bodies

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100122939 ◽

1992 ◽

Vol 50 (1) ◽

pp. 506-507

Author(s):

Robert L. Ochs

Keyword(s):

Electron Microscopy ◽

Structure And Function ◽

Nuclear Bodies ◽

Nuclear Interior ◽

Coiled Bodies ◽

Interchromatin Granules ◽

And Function ◽

Conventional Electron Microscopy ◽

Perichromatin Granules ◽

Perichromatin Fibrils

By conventional electron microscopy, the formed elements of the nuclear interior include the nucleolus, chromatin, interchromatin granules, perichromatin granules, perichromatin fibrils, and various types of nuclear bodies (Figs. 1a-c). Of these structures, all have been reasonably well characterized structurally and functionally except for nuclear bodies. The most common types of nuclear bodies are simple nuclear bodies and coiled bodies (Figs. 1a,c). Since nuclear bodies are small in size (0.2-1.0 μm in diameter) and infrequent in number, they are often overlooked or simply not observed in any random thin section. The rat liver hepatocyte in Fig. 1b is a case in point. Historically, nuclear bodies are more prominent in hyperactive cells, they often occur in proximity to nucleoli (Fig. 1c), and sometimes they are observed to “bud off” from the nucleolar surface.

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Crystal Structures of Fragments D and Double-D from Fibrinogen and Fibrin

Thrombosis and Haemostasis ◽

10.1055/s-0037-1615842 ◽

1999 ◽

Vol 82 (08) ◽

pp. 271-276 ◽

Cited By ~ 8

Author(s):

Glen Spraggon ◽

Stephen Everse ◽

Russell Doolittle

Keyword(s):

Electron Microscopy ◽

High Resolution ◽

Crystal Structures ◽

Structure And Function ◽

X Ray ◽

Long Period ◽

And Function

IntroductionAfter a long period of anticipation,1 the last two years have witnessed the first high-resolution x-ray structures of fragments from fibrinogen and fibrin.2-7 The results confirmed many aspects of fibrinogen structure and function that had previously been inferred from electron microscopy and biochemistry and revealed some unexpected features. Several matters have remained stubbornly unsettled, however, and much more work remains to be done. Here, we review several of the most significant findings that have accompanied the new x-ray structures and discuss some of the problems of the fibrinogen-fibrin conversion that remain unresolved. * Abbreviations: GPR—Gly-Pro-Arg-derivatives; GPRPam—Gly-Pro-Arg-Pro-amide; GHRPam—Gly-His-Arg-Pro-amide

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Morphological and Biochemical Features Affecting the Antithrombotic Properties of the Aorta in Adult Rabbits and Rabbit Pups

Thrombosis and Haemostasis ◽

10.1055/s-0037-1615116 ◽

1998 ◽

Vol 79 (05) ◽

pp. 1034-1040 ◽

Cited By ~ 17

Author(s):

E. Nitschmann ◽

L. Berry ◽

S. Bridge ◽

M. W. C. Hatton ◽

M. Richardson ◽

...

Keyword(s):

Electron Microscopy ◽

Gel Electrophoresis ◽

Arterial Wall ◽

Structure And Function ◽

Wall Structure ◽

Matrix Structure ◽

Antithrombotic Activity ◽

Antithrombin Activity ◽

And Function ◽

Biochemical Features

SummaryWe hypothesised that there are important physiologic differences in arterial wall structure and function with respect to antithrombotic activity in the very young (pre-puberty) compared to adults. Electron microscopy, gel electrophoresis, and activity assays were used to examine differences in aorta structure and function comparing prepubertal rabbits (pups) to adult rabbits. Differences in endothelial function, extracellular matrix structure, proteoglycan (PG) distribution and glycosaminoglycan (GAG) content and function were shown. In both intima and media, total PG, chondroitin sulfate (CS) PG and heparan sulfate (HS) PG content were significantly increased in pups compared to adult rabbits. These findings corresponded to increased concentrations by mass analyses of CS GAG and DS GAG in aortas from pups. There was also a significant increase in antithrombin activity in pups due to HS GAG. In conclusion, differences in both structure and antithrombin activity of aortas from pups compared to adult rabbits suggest that young arteries may have greater antithrombotic potential that is, at least in part, related to increased HS GAG.

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SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

10.1101/2020.05.13.093997 ◽

2020 ◽

Author(s):

Yupeng Wang ◽

Rosario B. Jaime-Lara ◽

Abhrarup Roy ◽

Ying Sun ◽

Xinyue Liu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Dna Sequences ◽

Cell Types ◽

Learning Models ◽

Cell Type ◽

Coding Sequences ◽

Sequence Features ◽

Cell Type Specific ◽

Different Cell Types

AbstractWe propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, sequential k-mer (k=5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers including gkm-SVM and DanQ, with regard to distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL is able to directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified according to their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL.

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Self-Supervised Representation Learning of Protein Tertiary Structures (PtsRep): Protein Engineering as A Case Study

10.1101/2020.12.22.423916 ◽

2020 ◽

Author(s):

Junwen Luo ◽

Yi Cai ◽

Jialin Wu ◽

Hongmin Cai ◽

Xiaofeng Yang ◽

...

Keyword(s):

Deep Learning ◽

Protein Engineering ◽

Structural Information ◽

Representation Learning ◽

Sequence Information ◽

Structural Representation ◽

Tertiary Structures ◽

Structural Space ◽

General Protein ◽

And Function

AbstractIn recent years, deep learning has been increasingly used to decipher the relationships among protein sequence, structure, and function. Thus far deep learning of proteins has mostly utilized protein primary sequence information, while the vast amount of protein tertiary structural information remains unused. In this study, we devised a self-supervised representation learning framework to extract the fundamental features of unlabeled protein tertiary structures (PtsRep), and the embedded representations were transferred to two commonly recognized protein engineering tasks, protein stability and GFP fluorescence prediction. On both tasks, PtsRep significantly outperformed the two benchmark methods (UniRep and TAPE-BERT), which are based on protein primary sequences. Protein clustering analyses demonstrated that PtsRep can capture the structural signals in proteins. PtsRep reveals an avenue for general protein structural representation learning, and for exploring protein structural space for protein engineering and drug design.

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Evaluation of neutrophil structure and function by electron microscopy: cytochemical studies

Journal of Immunological Methods ◽

10.1016/s0022-1759(99)00174-x ◽

1999 ◽

Vol 232 (1-2) ◽

pp. 169-178 ◽

Cited By ~ 8

Author(s):

John M Robinson ◽

Toshihiro Kobayashi ◽

Harumichi Seguchi ◽

Toshihiro Takizawa

Keyword(s):

Electron Microscopy ◽

Structure And Function ◽

And Function

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Electron microscopy of proteins macromolecular structure and function, volume 4

FEBS Letters ◽

10.1016/0014-5793(86)80241-1 ◽

1986 ◽

Vol 196 (1) ◽

pp. 183-184

Author(s):

PaulineM. Harrison

Keyword(s):

Electron Microscopy ◽

Structure And Function ◽

Macromolecular Structure ◽

And Function

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Canalicular domain structure and function in matrix-free hepatic spheroids

Biomaterials Science ◽

10.1039/c9bm01143a ◽

2020 ◽

Vol 8 (1) ◽

pp. 485-496 ◽

Cited By ~ 2

Author(s):

Vikas Raj Sharma ◽

Ananya Shrivastava ◽

Benoit Gallet ◽

Elizaveta Karepina ◽

Peggy Charbonnier ◽

...

Keyword(s):

Electron Microscopy ◽

Fluorescence Microscopy ◽

Light Sheet ◽

Structure And Function ◽

Bile Canaliculi ◽

Combined Use ◽

3D Electron Microscopy ◽

And Function ◽

Matrix Free ◽

Light Sheet Fluorescence Microscopy

The combined use of light sheet fluorescence microscopy and 3D electron microscopy enables to reveal the fine details of bile canaliculi structure and function in matrix-free hepatic spheroids.

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Effect of basolateral acidification on the frog oxynticopeptic cell

AJP Gastrointestinal and Liver Physiology ◽

10.1152/ajpgi.1990.259.4.g564 ◽

1990 ◽

Vol 259 (4) ◽

pp. G564-G570 ◽

Cited By ~ 1

Author(s):

S. Arvidsson ◽

K. Carter ◽

A. Yanaka ◽

S. Ito ◽

W. Silen

Keyword(s):

Electron Microscopy ◽

Gastric Mucosa ◽

Light And Electron Microscopy ◽

Structure And Function ◽

Intracellular Acidification ◽

Intracellular Acidosis ◽

Normal Morphology ◽

And Function ◽

Oxynticopeptic Cells

The effects of intracellular acidosis induced by acidification of the basolateral (nutrient) perfusate on the structure and function of the oxynticopeptic cell were studied in in vitro frog gastric mucosa. Changing the pH of the unbuffered nutrient perfusate (UNB) from 7.2 to 3.5 acidified the oxynticopeptic cell with no change in potential difference (PD) or resistance (R). Intracellular pH (pHi), PD, and R were 7.05 +/- 0.01, 16 +/- 1 mV, 165 +/- 7 omega.cm2 before and 6.44 +/- 0.01, 16 +/- 2 mV, 170 +/- 9 omega.cm2 after nutrient acidification. Acid secretion (H+) increased from 0.86 +/- 0.07 to 1.88 +/- 0.18 mu eq.cm-2.h-1. Addition of forskolin to tissues perfused with nutrient pH (pHn) 3.5 decreased PD to 2 +/- 2 mV and further increased H+ to 3.07 +/- 0.19 mu eq.cm-2.h-1. By light and electron microscopy oxynticopeptic cells perfused with UNB, pHn 3.5, appeared normal. Oxynticopeptic cells in tissues pretreated with omeprazole and then exposed to UNB, pHn 3.5, had extensive morphological damage. On increasing the pH of the nutrient perfusate from 3.5 to 7.2 there was prompt recovery of pHi in untreated and forskolin-stimulated mucosae (pHi 6.87 +/- 0.06 and 6.85 +/- 0.04) but no recovery of pHi in tissues pretreated with omeprazole or cimetidine (pHi 6.26 +/- 0.04 and 6.44 +/- 0.06, n = 6, 30 min after reexposure to UNB, pHn 7.2). We conclude that in a secreting mucosa intracellular acidification of the oxynticopeptic cell to pHi 6.4 is associated with normal morphology, PD, R, and increased H+, and that intracellular acidosis is not de facto deleterious.

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