scholarly journals Determining molecular archetype composition and expression from bulk tissues with unsupervised deconvolution

2021 ◽  
Author(s):  
Chiung-Ting Wu ◽  
Lulu Chen ◽  
David Herrington ◽  
Minjie Shen ◽  
Guoqiang Yu ◽  
...  
Keyword(s):  
Convex Analysis ◽  
Expression Patterns ◽  
Cell Types ◽  
Molecular Profiling ◽  
Ecological Systems ◽  
Dynamic State ◽  
Deconvolution Method ◽  
Benchmark Data ◽  
Individual Contributions ◽  
Scientific Hypotheses

Complex tissues are composite ecological systems whose components interact with each other to create a unique physiological or pathophysiological state distinct from that found in other tissue microenvironments. To explore this ground yet dynamic state, molecular profiling of bulk tissues and mathematical deconvolution can be jointly used to characterize heterogeneity as an aggregate of molecularly distinct tissue or cell subtypes. We first introduce an efficient and fully unsupervised deconvolution method, namely the Convex Analysis of Mixtures - CAM3.0, that may aid biologists to confirm existing or generate novel scientific hypotheses about complex tissues in many biomedical contexts. We then evaluate the CAM3.0 functional pipelines using both simulations and benchmark data. We also report diverse case studies on bulk tissues with unknown number, proportion and expression patterns of the molecular archetypes. Importantly, these preliminary results support the concept that expression patterns of molecular archetypes often reflect the interactive not individual contributions of many known or novel cell types, and unsupervised deconvolution would be more powerful in uncovering novel multicellular or subcellular archetypes.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

scholarly journals Multifaceted Functions of Platelets in Cancer: From Tumorigenesis to Liquid Biopsy Tool and Drug Delivery System

2020 ◽  
Vol 21 (24) ◽  
pp. 9585
Author(s):  
Melania Dovizio ◽  
Patrizia Ballerini ◽  
Rosa Fullone ◽  
Stefania Tacconelli ◽  
Annalisa Contursi ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Cancer Cells ◽  
Immune Cells ◽  
Expression Patterns ◽  
Cell Communication ◽  
Antiplatelet Agents ◽  
Cell Types ◽  
Disease Monitoring ◽  
Crucial Component ◽  
Numerous Cell

Platelets contribute to several types of cancer through plenty of mechanisms. Upon activation, platelets release many molecules, including growth and angiogenic factors, lipids, and extracellular vesicles, and activate numerous cell types, including vascular and immune cells, fibroblasts, and cancer cells. Hence, platelets are a crucial component of cell–cell communication. In particular, their interaction with cancer cells can enhance their malignancy and facilitate the invasion and colonization of distant organs. These findings suggest the use of antiplatelet agents to restrain cancer development and progression. Another peculiarity of platelets is their capability to uptake proteins and transcripts from the circulation. Thus, cancer-patient platelets show specific proteomic and transcriptomic expression patterns, a phenomenon called tumor-educated platelets (TEP). The transcriptomic/proteomic profile of platelets can provide information for the early detection of cancer and disease monitoring. Platelet ability to interact with tumor cells and transfer their molecular cargo has been exploited to design platelet-mediated drug delivery systems to enhance the efficacy and reduce toxicity often associated with traditional chemotherapy. Platelets are extraordinary cells with many functions whose exploitation will improve cancer diagnosis and treatment.

scholarly journals Electron Microscopic Immunogold Localization of Salivary Mucins MG1 and MG2 in Human Submandibular and Sublingual Glands

2003 ◽  
Vol 51 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Marco Piludu ◽  
Sean A. Rayment ◽  
Bing Liu ◽  
Gwynneth D. Offner ◽  
Frank G. Oppenheim ◽  
...  
Keyword(s):  
Salivary Glands ◽  
Expression Patterns ◽  
Cell Types ◽  
Mucous Cells ◽  
Electron Microscopic ◽  
Thin Sections ◽  
Dense Cores ◽  
Duct Cells ◽  
Rabbit Igg ◽  
Lowicryl K4m

The human salivary mucins MG1 and MG2 are well characterized biochemically and functionally. However, there is disagreement regarding their cellular and glandular sources. The aim of this study was to define the localization and distribution of these two mucins in human salivary glands using a postembedding immunogold labeling method. Normal salivary glands obtained at surgery were fixed in 3% paraformaldehyde-0.1% glutaraldehyde and embedded in Lowicryl K4M or LR Gold resin. Thin sections were labeled with rabbit antibodies to MG1 or to an N-terminal synthetic peptide of MG2, followed by gold-labeled goat anti-rabbit IgG. The granules of all mucous cells of the submandibular and sublingual glands were intensely reactive with anti-MG1. No reaction was detected in serous cells. With anti-MG2, the granules of both mucous and serous cells showed reactivity. The labeling was variable in both cell types, with mucous cells exhibiting a stronger reaction in some glands and serous cells in others. In serous granules, the electron-lucent regions were more reactive than the dense cores. Intercalated duct cells near the acini displayed both MG1 and MG2 reactivity in their apical granules. In addition, the basal and lateral membranes of intercalated duct cells were labeled with anti-MG2. These results confirm those of earlier studies on MG1 localization in mucous cells and suggest that MG2 is produced by both mucous and serous cells. They also indicate differences in protein expression patterns among salivary serous cells.

scholarly journals Mathematical modelling of promoter occupancies in MYC-dependent gene regulation

2017 ◽  
Vol 3 (2) ◽  
pp. 54
Author(s):  
Uwe Benary ◽  
Elmar Wolf ◽  
Jana Wolf
Keyword(s):  
Dna Binding ◽  
Mathematical Modelling ◽  
Expression Patterns ◽  
Cell Types ◽  
Osteosarcoma Cell ◽  
Genome Wide ◽  
Human Osteosarcoma Cell ◽  
Cell Type Specific ◽  
Binding Behaviour ◽  
Oncogene Protein

The human MYC proto-oncogene protein (MYC) is a transcription factor that plays a major role in the regulation of cell proliferation. Deregulation of MYC expression is often found in cancer. In the last years, several hypotheses have been proposed to explain cell type specific MYC target gene expression patterns despite genome wide DNA binding of MYC. In a recent publication, a mathematical modelling approach in combination with experimental data demonstrated that differences in MYC-DNA-binding affinity are sufficient to explain distinct promoter occupancies and allow stratification of distinct MYC-regulated biological processes at different MYC concentrations. Here, we extend the analysis of the published mathematical model of DNA-binding behaviour of MYC to demonstrate that the insights gained in the investigation of the human osteosarcoma cell line U2OS can be generalized to other human cell types.

scholarly journals Revealing immune responses in the Mycobacterium avium subsp. paratuberculosis-infected THP-1 cells using single cell RNA-sequencing

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254194
Author(s):  
Hong-Tae Park ◽  
Woo Bin Park ◽  
Suji Kim ◽  
Jong-Sung Lim ◽  
Gyoungju Nah ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Single Cell ◽  
Mycobacterium Avium ◽  
Expression Patterns ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Cell Type ◽  
Cytokines And Chemokines

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic and debilitating disease in ruminants. MAP is also considered to be a possible cause of Crohn’s disease in humans. However, few studies have focused on the interactions between MAP and human macrophages to elucidate the pathogenesis of Crohn’s disease. We sought to determine the initial responses of human THP-1 cells against MAP infection using single-cell RNA-seq analysis. Clustering analysis showed that THP-1 cells were divided into seven different clusters in response to phorbol-12-myristate-13-acetate (PMA) treatment. The characteristics of each cluster were investigated by identifying cluster-specific marker genes. From the results, we found that classically differentiated cells express CD14, CD36, and TLR2, and that this cell type showed the most active responses against MAP infection. The responses included the expression of proinflammatory cytokines and chemokines such as CCL4, CCL3, IL1B, IL8, and CCL20. In addition, the Mreg cell type, a novel cell type differentiated from THP-1 cells, was discovered. Thus, it is suggested that different cell types arise even when the same cell line is treated under the same conditions. Overall, analyzing gene expression patterns via scRNA-seq classification allows a more detailed observation of the response to infection by each cell type.

scholarly journals Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Low Cost ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Supervised Machine Learning ◽  
Melanoma Metastasis ◽  
Immunologic Research

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

scholarly journals Polycomb Requires Chaperonin Containing TCP-1 Subunit 7 for Maintaining Gene Silencing in Drosophila

2021 ◽  
Vol 9 ◽  
Author(s):  
Najma Shaheen ◽  
Jawad Akhtar ◽  
Zain Umer ◽  
Muhammad Haider Farooq Khan ◽  
Mahnoor Hussain Bakhtiari ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Expression Patterns ◽  
Cell Types ◽  
Polycomb Group ◽  
Specific Gene ◽  
Cellular Memory ◽  
Trithorax Group ◽  
T Complex ◽  
Ring Complex ◽  
Active Expression

In metazoans, heritable states of cell type-specific gene expression patterns linked with specialization of various cell types constitute transcriptional cellular memory. Evolutionarily conserved Polycomb group (PcG) and trithorax group (trxG) proteins contribute to the transcriptional cellular memory by maintaining heritable patterns of repressed and active expression states, respectively. Although chromatin structure and modifications appear to play a fundamental role in maintenance of repression by PcG, the precise targeting mechanism and the specificity factors that bind PcG complexes to defined regions in chromosomes remain elusive. Here, we report a serendipitous discovery that uncovers an interplay between Polycomb (Pc) and chaperonin containing T-complex protein 1 (TCP-1) subunit 7 (CCT7) of TCP-1 ring complex (TRiC) chaperonin in Drosophila. CCT7 interacts with Pc at chromatin to maintain repressed states of homeotic and non-homeotic targets of PcG, which supports a strong genetic interaction observed between Pc and CCT7 mutants. Depletion of CCT7 results in dissociation of Pc from chromatin and redistribution of an abundant amount of Pc in cytoplasm. We propose that CCT7 is an important modulator of Pc, which helps Pc recruitment at chromatin, and compromising CCT7 can directly influence an evolutionary conserved epigenetic network that supervises the appropriate cellular identities during development and homeostasis of an organism.

scholarly journals Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

2021 ◽  
Author(s):  
Milton Pividori ◽  
Sumei Lu ◽  
Binglan Li ◽  
Chun Su ◽  
Matthew E. Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Expression Patterns ◽  
Cell Types ◽  
Joint Analysis ◽  
Genetic Associations ◽  
Disease Etiology ◽  
Mediating Role ◽  
Gene Modules ◽  
Trait Associations

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

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