scholarly journals Dissecting the regulation and function of ATP at the single-cell level

PLoS Biology ◽  
2018 ◽  
Vol 16 (12) ◽  
pp. e3000095 ◽  
Author(s):  
Jianhan Zhang ◽  
Xu Han ◽  
Yihan Lin
Keyword(s):  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
And Function

scholarly journals Mitochondrial isolation: when size matters

2020 ◽  
Vol 5 ◽  
pp. 226
Author(s):  
Alexander G. Bury ◽  
Amy E. Vincent ◽  
Doug M. Turnbull ◽  
Paolo Actis ◽  
Gavin Hudson
Keyword(s):  
Mitochondrial Dysfunction ◽  
Single Cell ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Isolation Techniques ◽  
Cellular Environment ◽  
Dynamics And Function ◽  
And Function ◽  
Subcellular Resolution

Mitochondrial vitality is critical to cellular function, with mitochondrial dysfunction linked to a growing number of human diseases. Tissue and cellular heterogeneity, in terms of genetics, dynamics and function means that increasingly mitochondrial research is conducted at the single cell level. Whilst, there are several single-cell technologies that are currently available, each with their advantages, they cannot be easily adapted to study mitochondria with subcellular resolution. Here we review the current techniques and strategies for mitochondrial isolation, critically discussing each technology’s limitations for future mitochondrial research. Finally, we highlight and discuss the recent breakthroughs in sub-cellular isolation techniques, with a particular focus on nanotechnologies that enable the isolation of mitochondria, from subcellular compartments, with unprecedented spatial precision with minimal disruption to mitochondria and their immediate cellular environment.

scholarly journals Single-cell RNA-Seq reveals the link between CD45 isoforms and tumor-infiltrating T cells heterogeneity in liver cancer

2020 ◽  
Author(s):  
Biaofeng Zhou ◽  
Shang Liu ◽  
Liang Wu ◽  
Yan Sun ◽  
Jie Chen ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Lymphocytes ◽  
Single Cell ◽  
Cell Function ◽  
Effector Memory ◽  
Single Cell Level ◽  
Cell Level ◽  
Cd45 Isoforms ◽  
And Function

AbstractCD45 isoforms play a major role in characterizing T cell function, phenotype, and development. However, there is lacking comprehensive interrogation about the relationship between CD45 isoforms and T lymphocytes from cancer patients at the single-cell level yet. Here, we investigated the CD45 isoforms component of published 5,063 T cells of hepatocellular carcinoma (HCC), which has been assigned functional states. We found that the distribution of CD45 isoforms in T lymphocytes cells depended on tissue resource, cell type, and functional state. Further, we demonstrated that CD45RO and CD45RA dominate in characterizing the phenotype and function of T cell though multiple CD45 isoforms coexist in T cells, through a novel alternative splicing pattern analysis. We identified a novel development trajectory of tumor-infiltrating T cells from Tcm to Temra (effector memory T cells re-expresses CD45RA) after detecting two subpopulations in state of transition, Tcm (central memory T) and Tem (effector memory T). Temra, capable of high cytotoxic characteristics, was discovered to be associated with the stage of HCC and may be a target of immunotherapy. Our study presents a comprehension of the connection between CD45 isoforms and the function, states, sources of T lymphocytes cells in HCC patients at the single-cell level, providing novel insight for the effect of CD45 isoforms on T cell heterogeneity.

Cytoplasmic dynein

2011 ◽  
Vol 39 (5) ◽  
pp. 1169-1178 ◽  
Author(s):  
Victoria J. Allan
Keyword(s):  
Single Cell ◽  
Recent Progress ◽  
Motor Proteins ◽  
Molecular Motor ◽  
Cytoplasmic Dynein ◽  
Eukaryotic Cells ◽  
Single Cell Level ◽  
Cell Level ◽  
Wide Range ◽  
And Function

The organization and function of eukaryotic cells rely on the action of many different molecular motor proteins. Cytoplasmic dynein drives the movement of a wide range of cargoes towards the minus ends of microtubules, and these events are needed, not just at the single-cell level, but are vital for correct development. In the present paper, I review recent progress on understanding dynein's mechanochemistry, how it is regulated and how it binds to such a plethora of cargoes. The importance of a number of accessory factors in these processes is discussed.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

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