scholarly journals Single-Cell RNA Sequencing Reveals Heterogeneity Among Adult Mouse, Primate and Human Kidney Cells

2022 ◽  
Author(s):  
Shuai Chen ◽  
Jinling Liao ◽  
Yang Chen ◽  
Yufang Lv ◽  
Qiong Song ◽  
...  
Keyword(s):  
Animal Models ◽  
Renal Disease ◽  
Single Cell ◽  
Cynomolgus Monkey ◽  
Cellular Localization ◽  
Human Kidney ◽  
Renal Diseases ◽  
Kidney Cells ◽  
Single Cell Level ◽  
Cell Level

Abstract Multiple studies have been performed to map the kidney landscape of human and rodent, along with the development of sequencing technique. Although rodent disease models have been widely applied, many disadvantages also exist. Non-human primates (NHPs) are considered as the closest experimental animals to humans and show great advantages in the construction of animal models of human disease. Therefore, a comprehensive understanding of the heterogeneity and homogeneity between human and multiple animal kidney cells is important for further establishing animal models of human renal disease. Here, we generated the first single-cell transcriptome data of normal adult cynomolgus monkey kidney using 10x Genomics scRNA-seq platform. Then, we further performed an in-depth comparison across species at the single-cell level, and our analysis indicated that the gene expression of adult primate kidney cells showed a better correlation with human kidney than mouse kidney. Furthermore, our results demonstrated that the cellular localization of GWAS-identified renal disease genes showed differences across species. The cellular localization of blood pressure associated genes in human displayed similarity to cynomolgus monkey. This study provided a reliable reference for further studies associated with renal diseases on NHPs. In addition, our results also provided a novel insight into the choice of renal disease animal model and a detailed explanation for close genetic relationship between NHPs and human at a single cell level.

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.

scholarly journals Quality control of direct cell–mineral adhesion measurements in air and liquid using inverse AFM imaging

RSC Advances ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 5384-5392
Author(s):  
Abd Alaziz Abu Quba ◽  
Gabriele E. Schaumann ◽  
Mariam Karagulyan ◽  
Doerte Diehl
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
C Cell ◽  
Afm Imaging ◽  
Direct Cell ◽  
Mineral Interaction

Setup for a reliable cell-mineral interaction at the single-cell level, (a) study of the mineral by a sharp tip, (b) study of the bacterial modified probe by a characterizer, (c) cell-mineral interaction, (d) subsequent check of the modified probe.

scholarly journals Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Human Cell ◽  
Transcriptome Analysis ◽  
Single Cell Analysis ◽  
Embryonic Stem ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

scholarly journals AES and ToF-SIMS combination for single cell chemical imaging of gold nanoparticle-labeled Escherichia coli

2021 ◽  
Author(s):  
Cecile COURREGES ◽  
Mélanie Bonnecaze ◽  
Delphine Flahaut ◽  
Sophie Nolivos ◽  
Regis Grimaud ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Gold Nanoparticles ◽  
Single Cell ◽  
Electron Spectroscopy ◽  
Auger Electron ◽  
Chemical Imaging ◽  
Single Cell Level ◽  
Cell Level ◽  
Tof Sims ◽  
Escherichia Coli Cells

A chemical fingerprint of Escherichia coli cells surface labeled by gelatin coated gold nanoparticles was obtained by combining Auger Electron Spectroscopy (AES) for single cell level chemical images, and Time-of-Flight...

scholarly journals Exploring Human Skin Aging at the Single-Cell Level

2021 ◽  
Vol 56 (3) ◽  
pp. 253-254
Author(s):  
Mary Mohrin ◽  
Heinrich Jasper
Keyword(s):  
Single Cell ◽  
Human Skin ◽  
Skin Aging ◽  
Single Cell Level ◽  
Cell Level
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