scholarly journals GENE-45. DISSECTING THE DRIVERS OF ADULT H3K27M-GLIOMAS AT THE SINGLE-CELL LEVEL

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi107-vi107
Author(s):  
Ilon Liu ◽  
Byron Avihai ◽  
Johannes Gojo ◽  
Keith Ligon ◽  
Thomas Czech ◽  
...  
Keyword(s):  
Single Cell ◽  
Time Window ◽  
Molecular Entity ◽  
Single Cell Level ◽  
Cell Level ◽  
Developmental Context ◽  
Level Data ◽  
Cellular Context ◽  
Insight Into ◽  
Shared Gene

Abstract Diffuse midline gliomas are characterized by a lysine27-to-methionine mutation in histone H3 (H3K27M-glioma) and represent a highly aggressive molecular entity of high-grade gliomas. These mutations have primarily been described in children, but are also increasingly recognized in adults. In pediatric H3K27M-gliomas, the transcriptional architecture and cellular composition have been studied at the single-cell level. Data suggest that these tumors arise through malignant transformation of a glial progenitor during a specific neurodevelopmental time window. On the other hand, little is known about the architecture and cellular context of adult H3K27M-gliomas, and it remains to be elucidated whether they are driven by the same or by distinct oncogenic programs as their pediatric counterparts. Here, we utilize single-cell transcriptomics to characterize the transcriptional landscape of five H3K27M-gliomas from adult patients aged 22 to 56 years (median 33 years). We describe the specific cellular and microenvironmental architecture of the adult tumors, which comprises distinct populations of cancer and normal cells. We contrast our findings to 12 location-matched pediatric H3K27M-tumors from patients aged 2.5 to 15 years (median 8 years) to identify features related to tumorigenesis and developmental context in light of the shared hallmark H3K27M mutation. Our preliminary data indicate shared gene expression programs between adult and pediatric tumors. However, we find significant differences in the composition of the immune compartment as well as less pronounced differentiation programs in the adult tumors. Our findings provide an unprecedented insight into the composition of adult H3K27M-gliomas and advance our understanding of this molecular tumor class.

scholarly journals Discovering single-cell eQTLs from scRNA-seq data only

2021 ◽  
Author(s):  
Tianxing Ma ◽  
Haochen Li ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Negative Binomial ◽  
Single Cells ◽  
Single Cell Level ◽  
Cell Type ◽  
Paired Data ◽  
Cell Level ◽  
Specific Tumor ◽  
Cellular Context ◽  
Cell Type Specific

eQTL studies are essential for understanding genomic regulation. Effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a unique powerful tool for utilizing the huge scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle.

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...

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