scholarly journals SCISSOR™: a single-cell inferred site-specific omics resource for tumor microenvironment association study

NAR Cancer ◽  
2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Xiang Cui ◽  
Fei Qin ◽  
Xuanxuan Yu ◽  
Feifei Xiao ◽  
Guoshuai Cai
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Clinical Outcomes ◽  
Large Scale ◽  
Cell Types ◽  
Cell Interaction ◽  
Specific Cell ◽  
Dynamic Visualization ◽  
Tissue Specific ◽  
Cell Composition

Abstract Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor–tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes.

scholarly journals Adult tissue-resident stem cells—fact or fiction?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Deepa Bhartiya
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Adult Stem Cells ◽  
Cell Types ◽  
Specific Cell ◽  
Tissue Specific ◽  
Cardiac Tissues ◽  
Adult Tissues ◽  
Resident Stem Cells ◽  
Abundant Cytoplasm

AbstractLife-long tissue homeostasis of adult tissues is supposedly maintained by the resident stem cells. These stem cells are quiescent in nature and rarely divide to self-renew and give rise to tissue-specific “progenitors” (lineage-restricted and tissue-committed) which divide rapidly and differentiate into tissue-specific cell types. However, it has proved difficult to isolate these quiescent stem cells as a physical entity. Recent single-cell RNAseq studies on several adult tissues including ovary, prostate, and cardiac tissues have not been able to detect stem cells. Thus, it has been postulated that adult cells dedifferentiate to stem-like state to ensure regeneration and can be defined as cells capable to replace lost cells through mitosis. This idea challenges basic paradigm of development biology regarding plasticity that a cell enters point of no return once it initiates differentiation. The underlying reason for this dilemma is that we are putting stem cells and somatic cells together while processing for various studies. Stem cells and adult mature cell types are distinct entities; stem cells are quiescent, small in size, and with minimal organelles whereas the mature cells are metabolically active and have multiple organelles lying in abundant cytoplasm. As a result, they do not pellet down together when centrifuged at 100–350g. At this speed, mature cells get collected but stem cells remain buoyant and can be pelleted by centrifuging at 1000g. Thus, inability to detect stem cells in recently published single-cell RNAseq studies is because the stem cells were unknowingly discarded while processing and were never subjected to RNAseq. This needs to be kept in mind before proposing to redefine adult stem cells.

scholarly journals Bulk and Single-Cell Transcriptomics Identify Tobacco-Use Disparity in Lung Gene Expression of ACE2, the Receptor of 2019-nCov

2020 ◽  
Author(s):  
Guoshuai Cai
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
Smoking History ◽  
Normal Lung ◽  
Specific Cell ◽  
Susceptible Population ◽  
Former Smokers ◽  
Ace2 Gene

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed four large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

scholarly journals Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov

2020 ◽  
Author(s):  
Guoshuai Cai
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Smoking Status ◽  
Cell Types ◽  
Smoking History ◽  
Normal Lung ◽  
Specific Cell ◽  
Susceptible Population ◽  
Former Smokers

AbstractIn current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

scholarly journals A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings

2021 ◽  
Author(s):  
Hongru Shen ◽  
Xilin Shen ◽  
Mengyao Feng ◽  
Dan Wu ◽  
Chao Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Gene Interaction ◽  
Cell Types ◽  
Specific Cell ◽  
Expression Data ◽  
Gene Interaction Networks ◽  
Universal Approach ◽  
Cell Expression

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulation of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 13.7 million single-cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

scholarly journals Bulk and Single-Cell Transcriptomics Identify Tobacco-Use Disparity in Lung Gene Expression of ACE2, the Receptor of 2019-nCov

2020 ◽  
Author(s):  
Guoshuai Cai
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
Smoking History ◽  
Normal Lung ◽  
Specific Cell ◽  
Susceptible Population ◽  
Former Smokers ◽  
Ace2 Gene

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn&rsquo;t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (&gt;60 vs &lt;60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker&rsquo;s lung compared to non-smoker&rsquo;s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

scholarly journals Mapping of single-cell landscape of acral melanoma and analysis of molecular regulatory network of tumor microenvironment

2021 ◽  
Author(s):  
Zan He ◽  
Zijuan Xin ◽  
Xiangdong Fang ◽  
Hua Zhao
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Interaction Network ◽  
Cell Types ◽  
Cell Interaction ◽  
Malignant Cells ◽  
Acral Melanoma ◽  
Asian Populations ◽  
Specific Tumor ◽  
Cell Transcriptome

Melanoma is a type of skin malignant tumor with high invasiveness, high metastasis, and poor prognosis. The incidence of melanoma continues to increase. Among them, the subtype of acral melanoma (AM) is more common in Asian populations. AM has higher degree, low immunotherapy response rate. With the help of single-cell sequencing technology provides new technical means for tumor microenvironment research, so that we can more easily explore specific tumor types suitable immunotherapy targets. However, no complete single-cell level differentiation map exists for the AM tumor microenvironment (TME). In this study, we used AM related sample and used the 10x Genomics single-cell transcriptome platform to draw a specific single-cell map of AM, understand the cell composition of AM, and analyze the interaction and molecular regulation of AM TME. Nine cell types were identified, of which malignant cells accounted for the largest proportion, followed by fibroblasts. And the cell interaction network shows that malignant cells, macrophages, B cells, T cells and fibroblasts play important roles in AM TME. Our research provides systematic theoretical guidance for the diagnosis and treatment of acral melanoma.

scholarly journals A practical solution to pseudoreplication bias in single-cell studies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kip D. Zimmerman ◽  
Mark A. Espeland ◽  
Carl D. Langefeld
Keyword(s):  
Single Cell ◽  
Mixed Models ◽  
Random Effect ◽  
Cell Types ◽  
Error Rates ◽  
Specific Cell ◽  
Experimental Conditions ◽  
Type 1 Error ◽  
Sample Correlation ◽  
Inflated Type

AbstractCells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility. This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation. Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models. To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual. Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies.

scholarly journals Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types

2019 ◽  
Vol 2 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Jinchu Vijay ◽  
Marie-Frédérique Gauthier ◽  
Rebecca L. Biswell ◽  
Daniel A. Louiselle ◽  
Jeffrey J. Johnston ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Human Adipose Tissue ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Analysis ◽  
Disease Specific

scholarly journals Chimeric Maternal Cells with Tissue-Specific Antigen Expression and Morphology Are Common in Infant Tissues

2009 ◽  
Vol 12 (5) ◽  
pp. 337-346 ◽  
Author(s):  
Anne M. Stevens ◽  
Heidi M. Hermes ◽  
Meghan M. Kiefer ◽  
Joe C. Rutledge ◽  
J. Lee Nelson
Keyword(s):  
Islet Cells ◽  
Tissue Injury ◽  
Specific Antigen ◽  
Antigen Expression ◽  
Cell Types ◽  
Target Cells ◽  
Specific Cell ◽  
Tissue Specific ◽  
Renal Tubular Cells ◽  
Parenchymal Cells

Maternal microchimerism (MMc) has been purported to play a role in the pathogenesis of autoimmunity, but how a small number of foreign cells could contribute to chronic, systemic inflammation has not been explained. Reports of peripheral blood cells differentiating into tissue-specific cell types may shed light on the problem in that chimeric maternal cells could act as target cells within tissues. We investigated MMc in tissues from 7 male infants. Female cells, presumed maternal, were characterized by simultaneous immunohistochemistry and fluorescence in situ hybridization for X- and Y-chromosomes. Maternal cells constituted 0.017% to 1.9% of parenchymal cells and were found in all infants in liver, pancreas, lung, kidney, bladder, skin, and spleen. Maternal cells were differentiated: maternal hepatocytes in liver, renal tubular cells in kidney, and β-islet cells in pancreas. Maternal cells were not found in areas of tissue injury or inflammatory infiltrate. Maternal hematopoietic cells were found only in hearts from patients with neonatal lupus. Thus, differentiated maternal cells are present in multiple tissue types and occur independently of inflammation or tissue injury. Loss of tolerance to maternal parenchymal cells could lead to organ-specific “auto” inflammatory disease and elimination of maternal cells in areas of inflammation.

Mesenchymal Stem Cells and Cancer Stem Cells: An Overview of Tumor-Mesenchymal Stem Cell Interaction for therapeutic interventions

2021 ◽  
Vol 22 ◽  
Author(s):  
Soheila Montazersaheb ◽  
Ezzatollah Fathi ◽  
Ayoub Mamandi ◽  
Raheleh Farahzadi ◽  
Hamid Reza Heidari
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Tumor Microenvironment ◽  
Cancer Stem Cells ◽  
Tumor Cells ◽  
Cell Types ◽  
Cell Interaction ◽  
Therapeutic Interventions ◽  
Tumorigenic Potential ◽  
Different Types

: Tumors are made up of different types of cancer cells that contribute to tumor heterogeneity. Among these cells, cancer stem cells (CSCs) have a significant role in the onset of cancer and development. Like other stem cells, CSCs are characterized by the capacity for differentiation and self-renewal. A specific population of CSCs is constituted by mesenchymal stem cells (MSCs) that differentiate into mesoderm-specific cells. The pro-or anti-tumorigenic potential of MSCs on the proliferation and development of tumor cells has been reported as contradictory results. Also, tumor progression is specified by the corresponding tumor cells like the tumor microenvironment. The tumor microenvironment consists of a network of reciprocal cell types such as endothelial cells, immune cells, MSCs, and fibroblasts as well as growth factors, chemokines, and cytokines. In this review, recent findings related to the tumor microenvironment and associated cell populations, homing of MSCs to tumor sites, and interaction of MSCs with tumor cells will be discussed.

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