scholarly journals The Development of Single-Cell Metabolism and Its Role in Studying Cancer Emergent Properties

2022 ◽  
Vol 11 ◽  
Author(s):  
Dingju Wei ◽  
Meng Xu ◽  
Zhihua Wang ◽  
Jingjing Tong
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Immune Escape ◽  
Cell Metabolism ◽  
Single Cells ◽  
Metabolic Reprogramming ◽  
Biological Information ◽  
Emergent Properties ◽  
Anti Cancer ◽  
Cell Metabolomics

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

scholarly journals Metabolic Anti-Cancer Effects of Melatonin: Clinically Relevant Prospects

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3018
Author(s):  
Marek Samec ◽  
Alena Liskova ◽  
Lenka Koklesova ◽  
Kevin Zhai ◽  
Elizabeth Varghese ◽  
...  
Keyword(s):  
Cancer Metabolism ◽  
Glucose Transporter ◽  
Aerobic Glycolysis ◽  
Cell Metabolism ◽  
Lactate Production ◽  
Pentose Phosphate ◽  
Metabolic Reprogramming ◽  
Effective Prevention ◽  
Anti Cancer ◽  
Glucose 6 Phosphate Dehydrogenase

Metabolic reprogramming characterized by alterations in nutrient uptake and critical molecular pathways associated with cancer cell metabolism represents a fundamental process of malignant transformation. Melatonin (N-acetyl-5-methoxytryptamine) is a hormone secreted by the pineal gland. Melatonin primarily regulates circadian rhythms but also exerts anti-inflammatory, anti-depressant, antioxidant and anti-tumor activities. Concerning cancer metabolism, melatonin displays significant anticancer effects via the regulation of key components of aerobic glycolysis, gluconeogenesis, the pentose phosphate pathway (PPP) and lipid metabolism. Melatonin treatment affects glucose transporter (GLUT) expression, glucose-6-phosphate dehydrogenase (G6PDH) activity, lactate production and other metabolic contributors. Moreover, melatonin modulates critical players in cancer development, such as HIF-1 and p53. Taken together, melatonin has notable anti-cancer effects at malignancy initiation, progression and metastasing. Further investigations of melatonin impacts relevant for cancer metabolism are expected to create innovative approaches supportive for the effective prevention and targeted therapy of cancers.

scholarly journals Assessing the measurement transfer function of single-cell RNA sequencing

2016 ◽  
Author(s):  
Hannah R. Dueck ◽  
Rizi Ai ◽  
Adrian Camarena ◽  
Bo Ding ◽  
Reymundo Dominguez ◽  
...  
Keyword(s):  
Transfer Function ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Transfer Functions ◽  
Single Cells ◽  
Biological Information ◽  
Gene Detection ◽  
Single Cell Rna Sequencing ◽  
Scale Control

AbstractRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

scholarly journals Targeting T cell metabolism in the tumor microenvironment: an anti-cancer therapeutic strategy

2019 ◽  
Vol 38 (1) ◽  
Author(s):  
Zhongping Yin ◽  
Ling Bai ◽  
Wei Li ◽  
Tanlun Zeng ◽  
Huimin Tian ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Tumor Microenvironment ◽  
Metabolic Pathways ◽  
Cell Metabolism ◽  
Basic Research ◽  
Metabolic Reprogramming ◽  
Activated T Cells ◽  
Cell Functions ◽  
Anti Cancer

Abstract T cells play important roles in anti-tumor immunity. Emerging evidence has revealed that distinct metabolic changes impact the activation and differentiation of T cells. Tailoring immune responses by manipulating cellular metabolic pathways and the identification of new targets may provide new options for cancer immunotherapy. In this review, we focus on recent advances in the metabolic reprogramming of different subtypes of T cells and T cell functions. We summarize how metabolic pathways accurately regulate T cell development, differentiation, and function in the tumor microenvironment. Because of the similar metabolism in activated T cells and tumor cells, we also describe the effect of the tumor microenvironment on T cell metabolism reprogramming, which may provide strategies for maximal anti-cancer effects and enhancing the immunity of T cells. Thus, studies of T lymphocyte metabolism can not only facilitate the basic research of immune metabolism, but also provide potential targets for drug development and new strategies for clinical treatment of cancer.

Single-cell RNA sequencing analysis of mouse follicular somatic cells

2021 ◽  
Author(s):  
Sen Li ◽  
Lei-Ning Chen ◽  
Hai-Jing Zhu ◽  
Xie Feng ◽  
Feng-Yun Xie ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Somatic Cells ◽  
Metabolic Reprogramming ◽  
Follicle Development ◽  
Sequencing Analysis ◽  
Cell Clusters ◽  
New Genes ◽  
Variable Genes ◽  
Cell Cell

Abstract Within the development of ovarian follicle, in addition to cell proliferation and differentiation, sophisticated cell–cell cross talks are established among follicular somatic cells such as granulosa cells (GCs) and theca cells. To systematically reveal the cell differentiation and signal transductions in follicular somatic cells, we collected the mouse follicular somatic cells from secondary to ovulatory stage, and analyzed the single cell transcriptomes. Having data filtered and screened, we found 6883 high variable genes in 4888 single cells. Then follicular somatic cells were clustered into 26 cell clusters, including 18 GC clusters, 4 theca endocrine cell (TEC) clusters, and 4 other somatic cell clusters, which include immune cells and Acta2 positive theca externa cells. From our data, we found there was metabolic reprogramming happened during GC differentiation. We also found both Cyp19a1 and Cyp11a1 could be expressed in TECs. We analyzed the expression patterns of genes associated with cell–cell interactions such as steroid hormone receptor genes, insulin signaling genes, and cytokine/transformation growth factor beta associated genes in all cell clusters. Lastly, we clustered the highly variable genes into 300 gene clusters, which could be used to search new genes involved in follicle development. These transcriptomes of follicular somatic cells provide us potential clues to reveal how mammals regulating follicle development and could help us find targets to improve oocyte quality for women with low fertility.

scholarly journals Pairing Microwell Arrays with an Affordable, Semiautomated Single-Cell Aspirator for the Interrogation of Circulating Tumor Cell Heterogeneity

2020 ◽  
Vol 25 (2) ◽  
pp. 162-176
Author(s):  
Jacob J. Tokar ◽  
Charlotte N. Stahlfeld ◽  
Jamie M. Sperger ◽  
David J. Niles ◽  
David J. Beebe ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Single Cells ◽  
Rna Extraction ◽  
Circulating Tumor Cell ◽  
Bulk Sample ◽  
Live Cells ◽  
Target Cells ◽  
Castration Resistant Prostate Cancer ◽  
Background Population

Comprehensive analysis of tumor heterogeneity requires robust methods for the isolation and analysis of single cells from patient samples. An ideal approach would be fully compatible with downstream analytic methods, such as advanced genomic testing. These endpoints necessitate the use of live cells at high purity. A multitude of microfluidic circulating tumor cell (CTC) enrichment technologies exist, but many of those perform bulk sample enrichment and are not, on their own, capable of single-cell interrogation. To address this, we developed an affordable semiautomated single-cell aspirator (SASCA) to further enrich rare-cell populations from a specialized microwell array, per their phenotypic markers. Immobilization of cells within microwells, integrated with a real-time image processing software, facilitates the detection and precise isolation of targeted cells that have been optimally seeded into the microwells. Here, we demonstrate the platform capabilities through the aspiration of target cells from an impure background population, where we obtain purity levels of 90%–100% and demonstrate the enrichment of the target population with high-quality RNA extraction. A range of low cell numbers were aspirated using SASCA before undergoing whole transcriptome and genome analysis, exhibiting the ability to obtain endpoints from low-template inputs. Lastly, CTCs from patients with castration-resistant prostate cancer were isolated with this platform and the utility of this method was confirmed for rare-cell isolation. SASCA satisfies a need for an affordable option to isolate single cells or highly purified subpopulations of cells to probe complex mechanisms driving disease progression and resistance in patients with cancer.

scholarly journals From tumor cell metabolism to tumor immune escape

2013 ◽  
Vol 45 (1) ◽  
pp. 106-113 ◽  
Author(s):  
Martin Villalba ◽  
Moeez G. Rathore ◽  
Nuria Lopez-Royuela ◽  
Ewelina Krzywinska ◽  
Johan Garaude ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Immune Escape ◽  
Cell Metabolism ◽  
Tumor Immune Escape ◽  
Tumor Cell Metabolism

scholarly journals Dissecting the Invasion-Associated Long Non-coding RNAs Using Single-Cell RNA-Seq Data of Glioblastoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Bo Pang ◽  
Fei Quan ◽  
Yanyan Ping ◽  
Jing Hu ◽  
Yujia Lan ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Cell Invasion ◽  
Molecular Mechanisms ◽  
Single Cells ◽  
Tumor Cell Invasion ◽  
Rna Seq ◽  
Validation Data ◽  
Invasion And Migration ◽  
Non Coding Rnas

Glioblastoma (GBM) is characterized by rapid and lethal infiltration of brain tissue, which is the primary cause of treatment failure and deaths for GBM. Therefore, understanding the molecular mechanisms of tumor cell invasion is crucial for the treatment of GBM. In this study, we dissected the single-cell RNA-seq data of 3345 cells from four patients and identified dysregulated genes including long non-coding RNAs (lncRNAs), which were involved in the development and progression of GBM. Based on co-expression network analysis, we identified a module (M1) that significantly overlapped with the largest number of dysregulated genes and was confirmed to be associated with GBM invasion by integrating EMT signature, experiment-validated invasive marker and pseudotime trajectory analysis. Further, we denoted invasion-associated lncRNAs which showed significant correlations with M1 and revealed their gradually increased expression levels along the tumor cell invasion trajectory, such as VIM-AS1, WWTR1-AS1, and NEAT1. We also observed the contribution of higher expression of these lncRNAs to poorer survival of GBM patients. These results were mostly recaptured in another validation data of 7930 single cells from 28 GBM patients. Our findings identified lncRNAs that played critical roles in regulating or controlling cell invasion and migration of GBM and provided new insights into the molecular mechanisms underlying GBM invasion as well as potential targets for the treatment of GBM.

scholarly journals Single-Cell Analysis of Hyperthermic Intraperitoneal Chemotherapy Treated Tumors Reveals Distinct Cellular and Molecular Responses

2020 ◽  
Author(s):  
Max P. Horowitz ◽  
Zahraa Alali ◽  
Tyler Alban ◽  
Changjin Hong ◽  
Emily L. Esakov ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Single Cell ◽  
Intraperitoneal Chemotherapy ◽  
Hyperthermic Intraperitoneal Chemotherapy ◽  
Single Cells ◽  
Killer Cell ◽  
Tissue Response ◽  
Metabolic Reprogramming ◽  
Cell Populations ◽  
Cancer Tissue

SummaryHyperthermic intraperitoneal chemotherapy (HIPEC) has emerged as a clinical regimen that prolongs overall survival for patients with advanced Epithelial Ovarian Cancer (EOC). However, the mechanism of action of HIPEC remains poorly understood. To provide insights into the rapid changes that accompany HIPEC, tumors from five patients with high grade serous ovarian cancer were harvested from the omentum at time of debulking and after 90 minutes of HIPEC treatment. Specimens were rapidly dissociated into single cells and processed for single cell RNA-seq. Unbiased clustering identified 19 cell clusters that were annotated based on cellular transcriptome signatures to identify the epithelial, stromal, T and B immune cells, macrophages, and natural killer cell populations. Hallmark pathway analysis revealed heat shock, metabolic reprogramming, inflammatory, and EMT pathway enrichment in distinct cell populations upon HIPEC treatment. Collectively, our findings provide the foundation for mechanistic studies focused on how HIPEC orchestrates the ovarian cancer tissue response.

scholarly journals Anti-cancer effect of a novel 2,3-didithiocarbamate-substituted naphthoquinone as a tumor metabolic suppressor in vitro and in vivo

MedChemComm ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 632-638 ◽  
Author(s):  
Xianling Ning ◽  
Yunqiao Li ◽  
Hailong Qi ◽  
Ridong Li ◽  
Yan Jin ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Tumor Cells ◽  
Cell Metabolism ◽  
Anti Cancer ◽  
Tumor Cell Metabolism

Suppressing tumor cell metabolism is an attractive strategy for treating cancer. We identified a 2,3-didithiocarbamate-substituted naphthoquinone 3i that inhibited the proliferation of tumor cells by disturbing their metabolism.

scholarly journals Sparsely-Connected Autoencoder (SCA) for single cell RNAseq data mining

2020 ◽  
Author(s):  
L Alessandri ◽  
F Cordero ◽  
M Beccuti ◽  
N Licheri ◽  
M Arigoni ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Medical Personnel ◽  
Cellular Heterogeneity ◽  
Biological Information ◽  
The Neural Network ◽  
Rnaseq Data ◽  
Functional Features ◽  
Cell Subpopulations

AbstractSingle-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Although scRNAseq has some technical challenges, it would be of great interest being able to disclose biological information out of cell subpopulations, which can be defined by cluster analysis of scRNAseq data. In this manuscript, we evaluated the efficacy of sparsely-connected autoencoder (SCA) as tool for the functional mining of single cells clusters. We show that SCA can be uses as tool to uncover hidden features associated to scRNAseq data. Our approach is strengthened by two metrics, QCF and QCM, which respectively allow to evaluate the ability of SCA to reconstruct a cells cluster and to evaluate the overall quality of the neural network model. Our data indicate that SCA encoded spaces, derived by different experimentally validated data (TFs targets, miRNAs targets, Kinases targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

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