scholarly journals Single-Cell Landscapes of Primary Glioblastomas and Matched Organoids and Cell Lines Reveal Variable Retention of Inter- and Intra-Tumor Heterogeneity

2021 ◽  
Author(s):  
Veronique G. LeBlanc ◽  
Diane L. Trinh ◽  
Shaghayegh Aslanpour ◽  
Martha Hughes ◽  
Dorothea Livingstone ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Tumor Heterogeneity ◽  
Variable Retention ◽  
Primary Glioblastomas

Single-Cell Transcriptomics Identifies Intra-Tumor Heterogeneity in Human Myeloma Cell Lines

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3385-3385 ◽  
Author(s):  
Amit Kumar Mitra ◽  
Holly Stessman ◽  
Michael A. Linden ◽  
Brian Van Ness
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Lines ◽  
Tumor Heterogeneity ◽  
Myeloma Cell ◽  
Gene Model ◽  
Color Flow ◽  
Myeloma Cells ◽  
Genetic Signature ◽  
Human Myeloma Cell

Abstract Multiple myeloma (MM) is a plasma cell neoplasm with significant complexity and heterogeneity. Proteasome inhibitors (PI) including bortezomib (Velcade/Bz), carfilzomib (Kyprolis/Cz) and Ixazomib are effective chemotherapeutic agents in the treatment of MM, used alone or in combination with other anti-cancer agents. However, in spite of the recent improvements in treatment strategies, MM still remains a difficult disease to cure with median survival rate of around 7 years. In a recently published study, we have shown that the heterogeneity in response to proteasome inhibitor (PI)-based treatment in MM is governed by underlying molecular characteristics of the subclones within tumor population (Stessman et al. 2013). We confirmed the presence of residual resistant sub-population comprising up to 15% of the bulk Bz-sensitive cell population in drug-naïve MM tumors. We hypothesize that this pre-existing resistant sub-population may give rise to emerging resistance in course of treatment with PIs. In the current study, we used single cell transcriptomics analysis to identify tumor subclones within Human Myeloma Cell Lines (HMCLs) based on a 48-gene model of predictive genetic signature for baseline PI response. Automated single-cell capture and cDNA synthesis from cellular RNA were performed using Fluidigm’s C1TM Single-Cell Auto Prep System. The cDNA was then harvested and transferred to BioMark HD System for single-cell targeted high-throughput qPCR-based gene expression analysis of a 48 gene-panel using Fluidigm DELTAgene assays. Our 48-gene model combines our previously published 23 gene expression profiling (GEP) signature that could discriminate between sensitive and resistant responsiveness to Bz, and the Shaughnessy et al prognostic 17-gene GEP model along with control genes, including cell cycle genes, anti-apoptotic genes, proteasome subunit genes, house-keeping genes and internal negative controls. Based on the differential expression of these 48 genes used in the modeling, distinct subclonal populations were then identified using a combination of Fluidigm’s analysis software and the R Statistical analysis package. Further, a principal component analysis (PCA) score plot was generated as a two-dimensional grid to visualize the separate populations associated with resistant profiles. Finally, hierarchical clustering (HC) analysis was used to generate heat maps that group expression patterns associated with response. Our results demonstrated the presence of pre-existing subclones of cells within untreated myeloma cells with a characteristic genetic signature profile distinct from the pre-treatment overall (bulk) profile of myeloma cells. As an additional validation of subclonal architecture, we demonstrated the presence of subclones within HMCLs using multi-color flow cytometry. The results presented will help identify the presence and extent of intra-tumor heterogeneity in MM by single cell transcriptomics and may define residual pre-existing subclones resistant to PI therapies. Disclosures No relevant conflicts of interest to declare.

scholarly journals Single-cell landscapes of primary glioblastomas and matched organoids and cell lines reveal variable retention of inter- and intra-tumor heterogeneity

2021 ◽  
Author(s):  
VG LeBlanc ◽  
DL Trinh ◽  
S Aslanpour ◽  
M Hughes ◽  
D Livingstone ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Heterogeneity ◽  
Cellular Heterogeneity ◽  
Valuable Resource ◽  
Wild Type ◽  
Variable Retention ◽  
Cell Genome ◽  
Transcriptional State ◽  
Tumor Initiating Cell ◽  
Single Cell Genome

SummaryGlioblastomas (GBMs) are aggressive primary malignant brain tumors characterized by extensive levels of inter- and intra-tumor genetic and phenotypic heterogeneity. Patient-derived organoids (PDOs) have recently emerged as useful models to study such heterogeneity. Here, we present bulk exome as well as single-cell genome and transcriptome profiles of primary IDH wild type GBMs from ten patients, including two recurrent tumors, as well as PDOs and brain tumor-initiating cell (BTIC) lines derived from these patients. We find that PDOs are genetically similar to and variably retain gene expression characteristics of their parent tumors. At the phenotypic level, PDOs appear to exhibit similar levels of transcriptional heterogeneity as their parent tumors, whereas BTIC lines tend to be enriched for cells in a more uniform transcriptional state. The datasets introduced here will provide a valuable resource to help guide experiments using GBM-derived organoids, especially in the context of studying cellular heterogeneity.

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 Multiplexed profiling of single-cell extracellular vesicles secretion

2019 ◽  
Vol 116 (13) ◽  
pp. 5979-5984 ◽  
Author(s):  
Yahui Ji ◽  
Dongyuan Qi ◽  
Linmei Li ◽  
Haoran Su ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Extracellular Vesicles ◽  
Single Cell Analysis ◽  
Comprehensive Evaluation ◽  
Single Cells ◽  
Deep Understanding ◽  
Principal Component ◽  
Cell Heterogeneity ◽  
Indispensable Tool

Extracellular vesicles (EVs) are important intercellular mediators regulating health and diseases. Conventional methods for EV surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EV secretion. Herein, by using spatially patterned antibody barcodes, we realized multiplexed profiling of single-cell EV secretion from more than 1,000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to a deep understanding of previously undifferentiated single-cell heterogeneity underlying EV secretion. Notably, we observed that the decrement of certain EV phenotypes (e.g.,CD63+EV) was associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EV secretion and cytokines secretion simultaneously from the same single cells to investigate the multidimensional spectrum of cellular communications, from which we resolved tiered functional subgroups with distinct secretion profiles by visualized clustering and principal component analysis. In particular, we found that different cell subgroups dominated EV secretion and cytokine secretion. The technology introduced here enables a comprehensive evaluation of EV secretion heterogeneity at single-cell level, which may become an indispensable tool to complement current single-cell analysis and EV research.

scholarly journals PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Marilisa Montemurro ◽  
Elena Grassi ◽  
Carmelo Gabriele Pizzino ◽  
Andrea Bertotti ◽  
Elisa Ficarra ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Single Cell ◽  
Tumor Heterogeneity ◽  
Third Party ◽  
Sequencing Technology ◽  
Reduction Methods ◽  
Visualization Tools ◽  
Cell Subpopulations ◽  
Multiple Samples ◽  
Analytical Approaches

Abstract Background Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. Results We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. Conclusions PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.

Quantitative Analysis of Multiple Proteins of Different Invasive Tumor Cell Lines at the Same Single-Cell Level

Small ◽  
2018 ◽  
Vol 14 (17) ◽  
pp. 1703684 ◽  
Author(s):  
Xiangchun Zhang ◽  
Ru Liu ◽  
Qingming Shu ◽  
Qing Yuan ◽  
Gengmei Xing ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Tumor Cell ◽  
Single Cell ◽  
Cell Lines ◽  
Single Cell Level ◽  
Tumor Cell Lines ◽  
Cell Level ◽  
Invasive Tumor

scholarly journals Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

2020 ◽  
Author(s):  
Viacheslav Mylka ◽  
Jeroen Aerts ◽  
Irina Matetovici ◽  
Suresh Poovathingal ◽  
Niels Vandamme ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Comparative Analysis ◽  
Single Cell ◽  
Cell Lines ◽  
Clinical Studies ◽  
Clinical Samples ◽  
Rna Seq ◽  
Batch Effects ◽  
Single Cell Sequencing ◽  
Single Nucleus

ABSTRACTMultiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called ‘hashing’. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

scholarly journals Multiplexed Profiling of Single-cell Extracellular Vesicles Secretion

2018 ◽  
Author(s):  
Yahui Ji ◽  
Dongyuan Qi ◽  
Linmei Li ◽  
Haoran Su ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Extracellular Vesicles ◽  
Single Cells ◽  
Cell Communication ◽  
Deep Understanding ◽  
Surface Markers ◽  
Cell Heterogeneity ◽  
Health And Disease ◽  
Cytokines Secretion

AbstractExtracellular vesicles (EVs) are important intercellular mediators regulating health and disease. Conventional EVs surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EVs secretion. Herein, by using spatially patterned antibodies barcode, we realized multiplexed profiling of single-cell EVs secretion from more than 1000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to deep understanding of previously undifferentiated single cell heterogeneity underlying EVs secretion. Notably, we observed the decrement of certain EV phenotypes (e.g. CD63+EVs) were associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EVs secretion and cytokines secretion simultaneously from the same single cells to investigate multidimensional spectrum of intercellular communications, from which we resolved three functional subgroups with distinct secretion profiles by visualized clustering. In particular, we found EVs secretion and cytokines secretion were generally dominated by different cell subgroups. The technology introduced here enables comprehensive evaluation of EVs secretion heterogeneity at single cell level, which may become an indispensable tool to complement current single cell analysis and EV research.SignificanceExtracellular vesicles (EVs) are cell derived nano-sized particles medicating cell-cell communication and transferring biology information molecules like nucleic acids to regulate human health and disease. Conventional methods for EV surface markers profiling can’t tell the differences in the quantity and phenotypes of EVs secretion between cells. To address this need, we developed a platform for profiling an array of surface markers on EVs from large numbers of single cells, enabling more comprehensive monitoring of cellular communications. Single cell EVs secretion assay led to previously unobserved cell heterogeneity underlying EVs secretion, which might open up new avenues for studying cell communication and cell microenvironment in both basic and clinical research.

Sign in / Sign up

Export Citation Format

Share Document