scholarly journals Single Cell Transcriptional Signatures of the Human Placenta in Term and Preterm Parturition

2019 ◽  
Author(s):  
Roger Pique-Regi ◽  
Roberto Romero ◽  
Adi L.Tarca ◽  
Edward D. Sendler ◽  
Yi Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Single Cell ◽  
Human Placenta ◽  
Preterm Labor ◽  
Cell Types ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Activated T Cell

AbstractMore than 135 million births occur each year; yet, the molecular underpinnings of human parturition in gestational tissues, and in particular the placenta, are still poorly understood. The placenta is a complex heterogeneous organ including cells of both maternal and fetal origin, and insults that disrupt the maternal-fetal dialogue could result in adverse pregnancy outcomes such as preterm birth. There is limited knowledge of the cell type composition and transcriptional activity of the placenta and its compartments during physiologic and pathologic parturition. To fill this knowledge gap, we used scRNA-seq to profile the placental villous tree, basal plate, and chorioamniotic membranes of women with or without labor at term and those with preterm labor. Significant differences in cell type composition and transcriptional profiles were found among placental compartments and across study groups. For the first time, two cell types were identified: 1) lymphatic endothelial decidual cells in the chorioamniotic membranes, and 2) non-proliferative interstitial cytotrophoblasts in the placental villi. Maternal macrophages from the chorioamniotic membranes displayed the largest differences in gene expression (e.g. NFKB1) in both processes of labor; yet, specific gene expression changes were also detected in preterm labor. Importantly, several placental scRNA-seq transcriptional signatures were modulated with advancing gestation in the maternal circulation, and specific immune cell type signatures were increased with labor at term (NK-cell and activated T-cell) and with preterm labor (macrophage, monocyte, and activated T-cell). Herein, we provide a catalogue of cell types and transcriptional profiles in the human placenta, shedding light on the molecular underpinnings and non-invasive prediction of the physiologic and pathologic parturition.One sentence summaryThe common molecular pathway of parturition for both term and preterm spontaneous labor is characterized using single cell gene expression analysis of the human placenta.

scholarly journals Single cell transcriptional signatures of the human placenta in term and preterm parturition

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Roger Pique-Regi ◽  
Roberto Romero ◽  
Adi L Tarca ◽  
Edward D Sendler ◽  
Yi Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Human Placenta ◽  
Preterm Labor ◽  
Cell Types ◽  
Cell Type ◽  
Transcriptional Profiles ◽  
Cell Type Composition ◽  
Type Composition ◽  
Activated T Cell

More than 135 million births occur each year; yet, the molecular underpinnings of human parturition in gestational tissues, and in particular the placenta, are still poorly understood. The placenta is a complex heterogeneous organ including cells of both maternal and fetal origin, and insults that disrupt the maternal-fetal dialogue could result in adverse pregnancy outcomes such as preterm birth. There is limited knowledge of the cell type composition and transcriptional activity of the placenta and its compartments during physiologic and pathologic parturition. To fill this knowledge gap, we used scRNA-seq to profile the placental villous tree, basal plate, and chorioamniotic membranes of women with or without labor at term and those with preterm labor. Significant differences in cell type composition and transcriptional profiles were found among placental compartments and across study groups. For the first time, two cell types were identified: 1) lymphatic endothelial decidual cells in the chorioamniotic membranes, and 2) non-proliferative interstitial cytotrophoblasts in the placental villi. Maternal macrophages from the chorioamniotic membranes displayed the largest differences in gene expression (e.g. NFKB1) in both processes of labor; yet, specific gene expression changes were also detected in preterm labor. Importantly, several placental scRNA-seq transcriptional signatures were modulated with advancing gestation in the maternal circulation, and specific immune cell type signatures were increased with labor at term (NK-cell and activated T-cell signatures) and with preterm labor (macrophage, monocyte, and activated T-cell signatures). Herein, we provide a catalogue of cell types and transcriptional profiles in the human placenta, shedding light on the molecular underpinnings and non-invasive prediction of the physiologic and pathologic parturition.

scholarly journals LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data

2021 ◽  
Author(s):  
Wenjing Ma ◽  
Sumeet Sharma ◽  
Peng Jin ◽  
Shannon L Gourley ◽  
Zhaohui Qin
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Cell Type ◽  
Reference Dataset ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Gene

The rapid proliferation of single-cell RNA-sequencing (scRNA-seq) datasets have revealed cell heterogeneity at unprecedented scales. Several deconvolution methods have been developed to decompose bulk experiments to reveal cell type contributions. However, these methods lack power in identifying the accurate cell type composition when having a considerable amount of sub-cell types in the reference dataset. Here, we present LRcell, a R Bioconductor package (http://bioconductor.org/packages/release/bioc/html/LRcell.html) aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq differential gene expression experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses.

scholarly journals Comprehensive benchmarking of computational deconvolution of transcriptomics data

2020 ◽  
Author(s):  
Francisco Avila Cobos ◽  
José Alquicira-Hernandez ◽  
Joseph Powell ◽  
Pieter Mestdagh ◽  
Katleen De Preter
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Factors Affecting ◽  
Marker Selection ◽  
Cell Type Composition ◽  
Type Composition ◽  
Comparable Performance ◽  
Transcriptomics Data ◽  
Combined Impact

AbstractMany computational methods to infer cell type proportions from bulk transcriptomics data have been developed. Attempts comparing these methods revealed that the choice of reference marker signatures is far more important than the method itself. However, a thorough evaluation of the combined impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the results is still lacking.Using different single-cell RNA-sequencing (scRNA-seq) datasets, we generated hundreds of pseudo-bulk mixtures to evaluate the combined impact of these factors on the deconvolution results. Along with methods to perform deconvolution of bulk RNA-seq data we also included five methods specifically designed to infer the cell type composition of bulk data using scRNA-seq data as reference.Both bulk and single-cell deconvolution methods perform best when applied to data in linear scale and the choice of normalization can have a dramatic impact on the performance of some, but not all methods. Overall, single-cell methods have comparable performance to the best performing bulk methods and bulk methods based on semi-supervised approaches showed higher error and lower correlation values between the computed and the expected proportions. Moreover, failure to include cell types in the reference that are present in a mixture always led to substantially worse results, regardless of any of the previous choices. Taken together, we provide a thorough evaluation of the combined impact of the different factors affecting the computational deconvolution task across different datasets and propose general guidelines to maximize its performance.

scholarly journals Single-Cell RNA-Seq Reveals AML Cellular Hierarchies Relevant to Clinical Outcomes and Immunity

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 542-542
Author(s):  
Peter Van Galen ◽  
Volker Hovestadt ◽  
Marc Wadsworth II ◽  
Travis Hughes ◽  
Gabriel Kenneth Griffin ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
T Cell Activation ◽  
Cell Activation ◽  
Cell Types ◽  
Healthy Controls ◽  
Cell Type ◽  
Healthy Donors ◽  
Type Composition

Abstract Acute myeloid leukemia (AML) is a heterogeneous disease with functionally diverse cells. While primitive leukemia cells are thought to be responsible for clonal expansion, other cell types may play roles in immune evasion and paracrine signaling. To analyze the complex AML ecosystem, we developed a technology for high throughput single-cell RNA-sequencing (scRNA-seq) combined with single-cell genotyping to capture mutations in cancer driver genes. We used this technology to parse normal and malignant hematopoietic systems. We profiled 38,410 cells from bone marrow (BM) aspirates from five healthy donors and 16 AML patients that span different WHO subtypes and cytogenetic abnormalities. Within the normal donors, we identified 15 diverse hematopoietic cell types demarcated by established markers such as CD34 (HSC/Progenitors), CD14 (monocytes) and CD3 (T-cells), confirming expected differentiation trajectories. To systematically distinguish between malignant and normal cell types within tumors, we developed a machine learning classifier that integrated scRNA-seq and single-cell genotyping data. Malignant cells were classified into six types: HSC-like, progenitor-like, granulocyte macrophage progenitor (GMP)-like, promonocyte-like, monocyte-like and dendritic-like cells. Each cell type was represented by at least 1,000 cells and identified in at least ten patients. To assess the significance of these six malignant cell types, we estimated their abundance in an independent cohort of 179 AMLs that were analyzed by bulk RNA-seq (TCGA). We found that the cell type composition of a tumor closely correlates to its underlying genetic lesions. For example, RUNX1-RUNX1T1 translocations are associated with GMP-like cells and TP53 mutations with undifferentiated cells (P < 0.001). NPM1+FLT3-ITD mutated tumors are enriched for more primitive cells compared to NPM1+FLT3-TKD mutants, which may relate to the worse outcomes of patients with FLT3-ITD mutations. The correspondence between genetic lesions and tumor cell type composition can guide strategies for genotype-specific therapies that target appropriate cellular states. Further investigation of primitive cells showed that gene expression programs associated with stemness (e.g. EGR1, MSI2) are mutually exclusive with myeloid priming (e.g. MPO, ELANE) in primitive cells of healthy donors. In contrast, these programs are often co-expressed within the same individual AML cells. When we applied our single cell-derived gene signatures to the TCGA dataset, stratification of these bulk expression profiles showed that patients with HSC-like progenitors had significantly poorer outcomes than patients with GMP-like progenitors (P < 0.0001). Aberrant co-expression of stemness and myeloid programs may underlie simultaneous self-renewal and proliferation, and expression of myeloid priming factors may provide a therapeutic window to target primitive AML cells while sparing normal HSCs. Examination of T-cells in our single-cell dataset showed that AML patients have fewer CD8+ cytotoxic T-lymphocytes within the CD3+ T-cell compartment compared to healthy controls, which was validated by immunohistochemistry on BM core biopsies (69% in healthy controls vs. 54% in AML, P < 0.05). We observed increased CD25+FOXP3+ T-regulatory cells in AML patients (1.2% in healthy controls vs. 3.6% in AML, P < 0.001), indicating an immunosuppressive tumor environment. To investigate mechanisms of immunosuppression, we used a T-cell activation bioassay that reports Nuclear Factor of Activated T-cells (NFAT). We compared the immunosuppressive function of different AML cell types, and found that CD14+ monocyte-like cells most effectively inhibit T-cell activation (P < 0.0001). The malignant status of these differentiated AML cells was confirmed by genotyping, and they express multiple factors associated with immunosuppression and T-cell engagement, including TIM-3 (HAVCR2), HVEM (TNFRSF14), CD155 (PVR) and HLA-DR. These results suggest that AMLs can differentiate into monocyte-like cells that suppress T-cell activation. In conclusion, we use novel technologies to parse heterogeneous cell states within the AML ecosystem. Our findings nominate strategies for precision therapies targeting AML progenitors or immunosuppressive functions of their differentiated progeny. Disclosures Pozdnyakova: Promedior, Inc.: Consultancy. Lane:N-of-one: Consultancy; Stemline Therapeutics: Research Funding.

scholarly journals Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Travis S. Johnson ◽  
Shunian Xiang ◽  
Bryan R. Helm ◽  
Zachary B. Abrams ◽  
Peter Neidecker ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Brain Tissue ◽  
Cell Types ◽  
Brain Atlas ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Single Cell Rna Sequencing

Abstract Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.

scholarly journals DOP04 Single cell RNA sequencing identifies distinct intestinal inflammation patterns between primary sclerosing cholangitis-associated colitis and Ulcerative Colitis

2021 ◽  
Vol 15 (Supplement_1) ◽  
pp. S043-S043
Author(s):  
W Uniken Venema ◽  
A Bangma ◽  
M Van der Wijst ◽  
G Kats-Ugurlu ◽  
J Bjork ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Sclerosing Cholangitis ◽  
Intestinal Inflammation ◽  
Cell Types ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing

Abstract Background Primary sclerosing cholangitis (PSC) is an inflammatory disorder of the bile ducts. The etiology of PSC is unknown, but it is hypothesized that intestinal barrier dysfunction, as seen in inflammatory bowel disease (IBD), plays a role. Roughly 75% of PSC patients have concomitant IBD (PSC-IBD). PSC-IBD is phenotypically different from ulcerative colitis (UC) with predominantly right-sided disease and a higher risk for colorectal cancer. In this study we aim aim to find probable distinct pathomechanisms for PSC-IBD, by comparing gut mucosal biopsies between PSC-IBD and UC using single-cell RNA sequencing. Methods 47 gut mucosal samples from the colon of subjects with either PSC-IBD (n=24), UC (n=18) or non-IBD control (n=5) were collected, from which 28 were paired inflamed and non-inflamed. Whole biopsies were cryopreserved and dissociated into single cells using collagenase digestion. Library preparation was done using the 10x Genomics system and subsequent sequencing was performed on an MGI2000 sequencer. The ‘Seurat’ R package was used for analysis. Results A total of 75.078 high quality cells identified 38 distinct cell types. No differences in cell type composition were observed between PSC-IBD and UC. We did see different cell type composition and gene expression between inflamed and non-inflamed samples. For example, in PSC-IBD specifically, an enterocyte subtype defined by DUOX2-expression showed inflammatory pathways upon inflammation. UC inflammation, on the other hand, was characterized by involvement of BEST4+ enterocytes and inflammatory fibroblasts. In addition, activated B cells and IgA plasma cells expressed stress-related genes in PSC, but not in UC inflammation. Conclusion We show that intestinal inflammation in PSC-IBD is characterized by distinct, cell-specific gene expression patterns as compared to UC. This highlights differential cell types mediating inflammation between these IBDs. Our study provides insight in cellular mechanisms underlying intestinal disease in PSC, and may serve as a starting point for further studies, for example on the functions of DUOX2+ enterocytes.

scholarly journals SSBER: removing batch effect for single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yin Zhang ◽  
Fei Wang
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Batch Effect ◽  
Batch Effects ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Type Composition ◽  
Type Composition ◽  
Downstream Analysis ◽  
Sequencing Platforms

Abstract Background With the continuous maturity of sequencing technology, different laboratories or different sequencing platforms have generated a large amount of single-cell transcriptome sequencing data for the same or different tissues. Due to batch effects and high dimensions of scRNA data, downstream analysis often faces challenges. Although a number of algorithms and tools have been proposed for removing batch effects, the current mainstream algorithms have faced the problem of data overcorrection when the cell type composition varies greatly between batches. Results In this paper, we propose a novel method named SSBER by utilizing biological prior knowledge to guide the correction, aiming to solve the problem of poor batch-effect correction when the cell type composition differs greatly between batches. Conclusions SSBER effectively solves the above problems and outperforms other algorithms when the cell type structure among batches or distribution of cell population varies considerably, or some similar cell types exist across batches.

scholarly journals Inference of Cell Type Composition from Human Brain Transcriptomic Datasets Illuminates the Effects of Age, Manner of Death, Dissection, and Psychiatric Diagnosis

2016 ◽  
Author(s):  
Megan Hastings Hagenauer ◽  
Anton Schulmann ◽  
Jun Z. Li ◽  
Marquis P. Vawter ◽  
David M. Walsh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Blood Cell ◽  
Cell Types ◽  
Post Mortem ◽  
Cell Type ◽  
Cell Content ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPsychiatric illness is unlikely to arise from pathology occurring uniformly across all cell types in affected brain regions. Despite this, transcriptomic analyses of the human brain have typically been conducted using macro-dissected tissue due to the difficulty of performing single-cell type analyses with donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary cortical cell types in previous publications. Using this database, we predicted the relative cell type composition for 833 human cortical samples using microarray or RNA-Seq data from the Pritzker Consortium (GSE92538) or publicly-available databases (GSE53987, GSE21935, GSE21138, CommonMind Consortium). These predictions were generated by averaging normalized expression levels across transcripts specific to each cell type using our R-packageBrainInABlender(validated and publicly-released:https://github.com/hagenaue/BrainInABlender). Using this method, we found that the principal components of variation in the datasets strongly correlated with the neuron to glia ratio of the samples.This variability was not simply due to dissection – the relative balance of brain cell types appeared to be influenced by a variety of demographic, pre- and post-mortem variables. Prolonged hypoxia around the time of death predicted increased astrocytic and endothelial gene expression, illustrating vascular upregulation. Aging was associated with decreased neuronal gene expression. Red blood cell gene expression was reduced in individuals who died following systemic blood loss. Subjects with Major Depressive Disorder had decreased astrocytic gene expression, mirroring previous morphometric observations. Subjects with Schizophrenia had reduced red blood cell gene expression, resembling the hypofrontality detected in fMRI experiments. Finally, in datasets containing samples with especially variable cell content, we found that controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects. We conclude that accounting for cell type can greatly improve the interpretability of transcriptomic data.

scholarly journals Seasonal changes in gene expression represent cell-type composition in whole blood

2014 ◽  
Vol 23 (10) ◽  
pp. 2721-2728 ◽  
Author(s):  
S. De Jong ◽  
M. Neeleman ◽  
J. J. Luykx ◽  
M. J. Ten Berg ◽  
E. Strengman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Whole Blood ◽  
Seasonal Changes ◽  
Cell Type ◽  
Cell Type Composition ◽  
Type Composition

scholarly journals Assessing the Contribution of Relative Macrophage Frequencies to Subcutaneous Adipose Tissue

2021 ◽  
Vol 8 ◽  
Author(s):  
Marianthi Kalafati ◽  
Michael Lenz ◽  
Gökhan Ertaylan ◽  
Ilja C. W. Arts ◽  
Chris T. Evelo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Subcutaneous Adipose Tissue ◽  
Tissue Cell ◽  
Cell Type ◽  
Expression Of Genes ◽  
Cell Type Composition ◽  
Type Composition ◽  
Adipose Tissue Cell ◽  
Subcutaneous Adipose

Background: Macrophages play an important role in regulating adipose tissue function, while their frequencies in adipose tissue vary between individuals. Adipose tissue infiltration by high frequencies of macrophages has been linked to changes in adipokine levels and low-grade inflammation, frequently associated with the progression of obesity. The objective of this project was to assess the contribution of relative macrophage frequencies to the overall subcutaneous adipose tissue gene expression using publicly available datasets.Methods: Seven publicly available microarray gene expression datasets from human subcutaneous adipose tissue biopsies (n = 519) were used together with TissueDecoder to determine the adipose tissue cell-type composition of each sample. We divided the subjects in four groups based on their relative macrophage frequencies. Differential gene expression analysis between the high and low relative macrophage frequencies groups was performed, adjusting for sex and study. Finally, biological processes were identified using pathway enrichment and network analysis.Results: We observed lower frequencies of adipocytes and higher frequencies of adipose stem cells in individuals characterized by high macrophage frequencies. We additionally studied whether, within subcutaneous adipose tissue, interindividual differences in the relative frequencies of macrophages were reflected in transcriptional differences in metabolic and inflammatory pathways. Adipose tissue of individuals with high macrophage frequencies had a higher expression of genes involved in complement activation, chemotaxis, focal adhesion, and oxidative stress. Similarly, we observed a lower expression of genes involved in lipid metabolism, fatty acid synthesis, and oxidation and mitochondrial respiration.Conclusion: We present an approach that combines publicly available subcutaneous adipose tissue gene expression datasets with a deconvolution algorithm to calculate subcutaneous adipose tissue cell-type composition. The results showed the expected increased inflammation gene expression profile accompanied by decreased gene expression in pathways related to lipid metabolism and mitochondrial respiration in subcutaneous adipose tissue in individuals characterized by high macrophage frequencies. This approach demonstrates the hidden strength of reusing publicly available data to gain cell-type-specific insights into adipose tissue function.

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