scholarly journals High-resolution DNA accessibility profiles increase the discovery and interpretability of genetic associations

2016 ◽  
Author(s):  
Aviv Madar ◽  
Diana Chang ◽  
Feng Gao ◽  
Aaron J. Sams ◽  
Yedael Y. Waldman ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Immune Cell ◽  
Cell Types ◽  
Genetic Associations ◽  
Cell Type ◽  
Adaptive Immune ◽  
Adaptive Immune Cell ◽  
Significance Threshold ◽  
Manhattan Plot ◽  
And Function

AbstractGenetic risk for common autoimmune diseases is influenced by hundreds of small effect, mostly non-coding variants, enriched in regulatory regions active in adaptive-immune cell types. DNaseI hypersensitivity sites (DHSs) are a genomic mark for regulatory DNA. Here, we generated a single DHSs annotation from fifteen deeply sequenced DNase-seq experiments in adaptive-immune as well as non-immune cell types. Using this annotation we quantified accessibility across cell types in a matrix format amenable to statistical analysis, deduced the subset of DHSs unique to adaptive-immune cell types, and grouped DHSs by cell-type accessibility profiles. Measuring enrichment with cell-type-specific TF binding sites as well as proximal gene expression and function, we show that accessibility profiles grouped DHSs into coherent regulatory functions. Using the adaptive-immune-specific DHSs as input (0.37% of genome), we associated DHSs to six autoimmune diseases with GWAS data. Associated loci showed higher replication rates when compared to loci identified by GWAS or by considering all DHSs, allowing the additional discovery of 327 loci (FDR<0.005) below typical GWAS significance threshold, 52 of which are novel and replicating discoveries. Finally, we integrated DHS associations from six autoimmune diseases, using a network model (bird’-eye view) and a regulatory Manhattan plot schema (per locus). Taken together, we described and validated a strategy to leverage finely resolved regulatory priors, enhancing the discovery, interpretability, and resolution of genetic associations, and providing actionable insights for follow up work.

scholarly journals Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile

2019 ◽  
Vol 79 (3) ◽  
pp. 379-386 ◽  
Author(s):  
Brian Skaug ◽  
Dinesh Khanna ◽  
William R Swindell ◽  
Monique E Hinchcliff ◽  
Tracy M Frech ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systemic Sclerosis ◽  
T Cell ◽  
Disease Duration ◽  
Immune Cell ◽  
Study Cohort ◽  
Progression Rate ◽  
Cell Type ◽  
Adaptive Immune ◽  
Adaptive Immune Cell

ObjectivesDetermine global skin transcriptome patterns of early diffuse systemic sclerosis (SSc) and how they differ from later disease.MethodsSkin biopsy RNA from 48 patients in the Prospective Registry for Early Systemic Sclerosis (PRESS) cohort (mean disease duration 1.3 years) and 33 matched healthy controls was examined by next-generation RNA sequencing. Data were analysed for cell type-specific signatures and compared with similarly obtained data from 55 previously biopsied patients in Genetics versus Environment in Scleroderma Outcomes Study cohort with longer disease duration (mean 7.4 years) and their matched controls. Correlations with histological features and clinical course were also evaluated.ResultsSSc patients in PRESS had a high prevalence of M2 (96%) and M1 (94%) macrophage and CD8 T cell (65%), CD4 T cell (60%) and B cell (69%) signatures. Immunohistochemical staining of immune cell markers correlated with the gene expression-based immune cell signatures. The prevalence of immune cell signatures in early diffuse SSc patients was higher than in patients with longer disease duration. In the multivariable model, adaptive immune cell signatures were significantly associated with shorter disease duration, while fibroblast and macrophage cell type signatures were associated with higher modified Rodnan Skin Score (mRSS). Immune cell signatures also correlated with skin thickness progression rate prior to biopsy, but did not predict subsequent mRSS progression.ConclusionsSkin in early diffuse SSc has prominent innate and adaptive immune cell signatures. As a prominently affected end organ, these signatures reflect the preceding rate of disease progression. These findings could have implications in understanding SSc pathogenesis and clinical trial design.

scholarly journals Fibroblasts Influence the Efficacy, Resistance, and Future Use of Vaccines and Immunotherapy in Cancer Treatment

Vaccines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Bailee H. Sliker ◽  
Paul M. Campbell
Keyword(s):  
Transforming Growth Factor ◽  
Transforming Growth Factor Β ◽  
Cell Types ◽  
Fibroblast Activation Protein ◽  
Cancer Associated Fibroblasts ◽  
Cell Type ◽  
Adaptive Immune ◽  
Adaptive Immune Cells ◽  
New Research ◽  
Tumor Types

Tumors are composed of not only epithelial cells but also many other cell types that contribute to the tumor microenvironment (TME). Within this space, cancer-associated fibroblasts (CAFs) are a prominent cell type, and these cells are connected to an increase in tumor progression as well as alteration of the immune landscape present in and around the tumor. This is accomplished in part by their ability to alter the presence of both innate and adaptive immune cells as well as the release of various chemokines and cytokines, together leading to a more immunosuppressive TME. Furthermore, new research implicates CAFs as players in immunotherapy response in many different tumor types, typically by blunting their efficacy. Fibroblast activation protein (FAP) and transforming growth factor β (TGF-β), two major CAF proteins, are associated with the outcome of different immunotherapies and, additionally, have become new targets themselves for immune-based strategies directed at CAFs. This review will focus on CAFs and how they alter the immune landscape within tumors, how this affects response to current immunotherapy treatments, and how immune-based treatments are currently being harnessed to target the CAF population itself.

scholarly journals Epigenetic reprogramming of cell identity: lessons from development for regenerative medicine

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Amitava Basu ◽  
Vijay K. Tiwari
Keyword(s):  
Regenerative Medicine ◽  
Cell Types ◽  
Direct Conversion ◽  
Cellular Reprogramming ◽  
Specific Cell ◽  
Cell Type ◽  
Pathological Conditions ◽  
Gene Regulatory ◽  
Induced Pluripotent ◽  
And Function

AbstractEpigenetic mechanisms are known to define cell-type identity and function. Hence, reprogramming of one cell type into another essentially requires a rewiring of the underlying epigenome. Cellular reprogramming can convert somatic cells to induced pluripotent stem cells (iPSCs) that can be directed to differentiate to specific cell types. Trans-differentiation or direct reprogramming, on the other hand, involves the direct conversion of one cell type into another. In this review, we highlight how gene regulatory mechanisms identified to be critical for developmental processes were successfully used for cellular reprogramming of various cell types. We also discuss how the therapeutic use of the reprogrammed cells is beginning to revolutionize the field of regenerative medicine particularly in the repair and regeneration of damaged tissue and organs arising from pathological conditions or accidents. Lastly, we highlight some key challenges hindering the application of cellular reprogramming for therapeutic purposes.

scholarly journals Jointly leveraging spatial transcriptomics and deep learning models for pathology image annotation improves cell type identification over either approach alone.

2021 ◽  
Author(s):  
Asif Zubair ◽  
Richard H. Chapple ◽  
Sivaraman Natarajan ◽  
William C. Wright ◽  
Min Pan ◽  
...  
Keyword(s):  
Immune Cell ◽  
Image Annotation ◽  
Cell Types ◽  
Tissue Cell ◽  
Cell Type ◽  
Spatially Resolved ◽  
Transcriptomics Data ◽  
Diagnostic Applications ◽  
The Many ◽  
Level Performance

The disorganization of cell types within tissues underlies many human diseases and has been studied for over a century using the conventional tools of pathology, including tissue-marking dyes such as the H&E stain. Recently, spatial transcriptomics technologies were developed that can measure spatially resolved gene expression directly in pathology-stained tissues sections, revealing cell types and their dysfunction in unprecedented detail. In parallel, artificial intelligence (AI) has approached pathologist-level performance in computationally annotating H&E images of tissue sections. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and AI-based pathology has performed less impressively outside their training datasets. Here, we describe a methodology that can computationally integrate AI-annotated pathology images with spatial transcriptomics data to markedly improve inferences of tissue cell type composition made over either class of data alone. We show that this methodology can identify regions of clinically relevant tumor immune cell infiltration, which is predictive of response to immunotherapy and was missed by an initial pathologist's manual annotation. Thus, combining spatial transcriptomics and AI-based image annotation has the potential to exceed pathologist-level performance in clinical diagnostic applications and to improve the many applications of spatial transcriptomics that rely on accurate cell type annotations.

scholarly journals MIXTURE: an improved algorithm for immune tumor microenvironment estimation based on gene expression data

2019 ◽  
Author(s):  
Elmer A. Fernández ◽  
Yamil D. Mahmoud ◽  
Florencia Veigas ◽  
Darío Rocha ◽  
Mónica Balzarini ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cell ◽  
Therapy Response ◽  
Cell Types ◽  
Gene Signature ◽  
Response To Therapy ◽  
Support Vector ◽  
Data Sets ◽  
Cell Type ◽  
Before And After

AbstractRNA sequencing has proved to be an efficient high-throughput technique to robustly characterize the presence and quantity of RNA in tumor biopsies at a given time. Importantly, it can be used to computationally estimate the composition of the tumor immune infiltrate and to infer the immunological phenotypes of those cells. Given the significant impact of anti-cancer immunotherapies and the role of the associated immune tumor microenvironment (ITME) on its prognosis and therapy response, the estimation of the immune cell-type content in the tumor is crucial for designing effective strategies to understand and treat cancer. Current digital estimation of the ITME cell mixture content can be performed using different analytical tools. However, current methods tend to over-estimate the number of cell-types present in the sample, thus under-estimating true proportions, biasing the results. We developed MIXTURE, a noise-constrained recursive feature selection for support vector regression that overcomes such limitations. MIXTURE deconvolutes cell-type proportions of bulk tumor samples for both RNA microarray or RNA-Seq platforms from a leukocyte validated gene signature. We evaluated MIXTURE over simulated and benchmark data sets. It overcomes competitive methods in terms of accuracy on the true number of present cell-types and proportions estimates with increased robustness to estimation bias. It also shows superior robustness to collinearity problems. Finally, we investigated the human immune microenvironment of breast cancer, head and neck squamous cell carcinoma, and melanoma biopsies before and after anti-PD-1 immunotherapy treatment revealing associations to response to therapy which have not seen by previous methods.

scholarly journals Profound functional and molecular diversity of mitochondria revealed by cell type-specific profiling in vivo

2018 ◽  
Author(s):  
Caroline Fecher ◽  
Laura Trovò ◽  
Stephan A. Müller ◽  
Nicolas Snaidero ◽  
Jennifer Wettmarshausen ◽  
...  
Keyword(s):  
Molecular Diversity ◽  
Molecular Level ◽  
Cell Types ◽  
Long Chain Fatty Acids ◽  
Cell Type ◽  
Mitochondrial Proteome ◽  
Novel Approach ◽  
Cell Type Specific ◽  
And Function

AbstractMitochondria vary in morphology and function in different tissues, however little is known about their molecular diversity among cell types. To investigate mitochondrial diversity in vivo, we developed an efficient protocol to isolate cell type-specific mitochondria based on a new MitoTag mouse. We profiled the mitochondrial proteome of three major neural cell types in cerebellum and identified a substantial number of differential mitochondrial markers for these cell types in mice and humans. Based on predictions from these proteomes, we demonstrate that astrocytic mitochondria metabolize long-chain fatty acids more efficiently than neurons. Moreover, we identified Rmdn3 as a major determinant of ER-mitochondria proximity in Purkinje cells. Our novel approach enables exploring mitochondrial diversity on the functional and molecular level in many in vivo contexts.

scholarly journals Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Prashant Rajbhandari ◽  
Douglas Arneson ◽  
Sydney K Hart ◽  
In Sook Ahn ◽  
Graciel Diamante ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Single Cell Analysis ◽  
Metabolic Response ◽  
Cell Types ◽  
Specific Cell ◽  
Beneficial Effects ◽  
Thermogenic Adipocytes ◽  
And Function

Immune cells are vital constituents of the adipose microenvironment that influence both local and systemic lipid metabolism. Mice lacking IL10 have enhanced thermogenesis, but the roles of specific cell types in the metabolic response to IL10 remain to be defined. We demonstrate here that selective loss of IL10 receptor α in adipocytes recapitulates the beneficial effects of global IL10 deletion, and that local crosstalk between IL10-producing immune cells and adipocytes is a determinant of thermogenesis and systemic energy balance. Single Nuclei Adipocyte RNA-sequencing (SNAP-seq) of subcutaneous adipose tissue defined a metabolically-active mature adipocyte subtype characterized by robust expression of genes involved in thermogenesis whose transcriptome was selectively responsive to IL10Rα deletion. Furthermore, single-cell transcriptomic analysis of adipose stromal populations identified lymphocytes as a key source of IL10 production in response to thermogenic stimuli. These findings implicate adaptive immune cell-adipocyte communication in the maintenance of adipose subtype identity and function.

Sign in / Sign up

Export Citation Format

Share Document