scholarly journals Immune Cell Degranulation in Fungal Host Defence

2021 ◽  
Vol 7 (6) ◽  
pp. 484
Author(s):  
Adley CH. Mok ◽  
Christopher H. Mody ◽  
Shu Shun Li
Keyword(s):  
Fungal Pathogens ◽  
Immune Cell ◽  
Cell Types ◽  
Host Defence ◽  
Secretory Vesicles ◽  
Cell Type ◽  
Fungal Host ◽  
Granule Morphology ◽  
Membrane Bound

Humans have developed complex immune systems that defend against invading microbes, including fungal pathogens. Many highly specialized cells of the immune system share the ability to store antimicrobial compounds in membrane bound organelles that can be immediately deployed to eradicate or inhibit growth of invading pathogens. These membrane-bound organelles consist of secretory vesicles or granules, which move to the surface of the cell, where they fuse with the plasma membrane to release their contents in the process of degranulation. Lymphocytes, macrophages, neutrophils, mast cells, eosinophils, and basophils all degranulate in fungal host defence. While anti-microbial secretory vesicles are shared among different immune cell types, information about each cell type has emerged independently leading to an uncoordinated and confusing classification of granules and incomplete description of the mechanism by which they are deployed. While there are important differences, there are many similarities in granule morphology, granule content, stimulus for degranulation, granule trafficking, and release of granules against fungal pathogens. In this review, we describe the similarities and differences in an attempt to translate knowledge from one immune cell to another that may facilitate further studies in the context of fungal host defence.

Cytoplasmic, nuclear, membrane-bound and secreted [35S]methionine-labelled polypeptide pattern in differentiating fibroblast stem cells in vitro

1989 ◽  
Vol 92 (2) ◽  
pp. 231-239
Author(s):  
P.I. Francz ◽  
K. Bayreuther ◽  
H.P. Rodemann
Keyword(s):  
Protein Fraction ◽  
Fibroblast Cell ◽  
Cell Types ◽  
Specific Marker ◽  
Human Skin Fibroblast ◽  
Nuclear Fraction ◽  
Cell Type ◽  
Marker Proteins ◽  
Cell Type Specific ◽  
Membrane Bound

Methods for the selective enrichment of various subpopulations of the human skin fibroblast cell line HH-8 have been developed. These methods permit the selection of homogeneous populations of the three mitotic fibroblast cell types MF I, II and III, and the four postmitotic cell types PMF IV, V, VI and VII. These seven cell types exhibit differentiation-dependent and cell-type-specific patterns of [35S]methionine-labelled polypeptides in total soluble cytoplasmic and nuclear proteins, also in membrane-bound proteins, and in secreted proteins. In the differentiation sequence MF II-MF III-PMF IV - PMF V - PMF VI 14 cell-type-specific marker proteins have been found in the cytoplasmic and nuclear fraction, also 24 cell-type-specific marker proteins have been found in the membrane-bound protein fraction, and 11 cell-type-specific marker proteins in the secreted protein fraction. Markers in spontaneously arising and experimentally selected or induced populations of a single fibroblast cell type were found to be identical.

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 Revisiting the role of IRF3 in inflammation and immunity by conditional and specifically targeted gene ablation in mice

2018 ◽  
Vol 115 (20) ◽  
pp. 5253-5258 ◽  
Author(s):  
Hideyuki Yanai ◽  
Shiho Chiba ◽  
Sho Hangai ◽  
Kohei Kometani ◽  
Asuka Inoue ◽  
...  
Keyword(s):  
Immune Cell ◽  
Cell Types ◽  
Type I ◽  
Type I Ifn ◽  
Cell Type ◽  
Gene Ablation ◽  
Cell Type Specific

IFN regulatory factor 3 (IRF3) is a transcription regulator of cellular responses in many cell types that is known to be essential for innate immunity. To confirm IRF3’s broad role in immunity and to more fully discern its role in various cellular subsets, we engineered Irf3-floxed mice to allow for the cell type-specific ablation of Irf3. Analysis of these mice confirmed the general requirement of IRF3 for the evocation of type I IFN responses in vitro and in vivo. Furthermore, immune cell ontogeny and frequencies of immune cell types were unaffected when Irf3 was selectively inactivated in either T cells or B cells in the mice. Interestingly, in a model of lipopolysaccharide-induced septic shock, selective Irf3 deficiency in myeloid cells led to reduced levels of type I IFN in the sera and increased survival of these mice, indicating the myeloid-specific, pathogenic role of the Toll-like receptor 4–IRF3 type I IFN axis in this model of sepsis. Thus, Irf3-floxed mice can serve as useful tool for further exploring the cell type-specific functions of this transcription factor.

scholarly journals Northstar enables automatic classification of known and novel cell types from tumor samples

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Fabio Zanini ◽  
Bojk A. Berghuis ◽  
Robert C. Jones ◽  
Benedetta Nicolis di Robilant ◽  
Rachel Yuan Nong ◽  
...  
Keyword(s):  
Cell Types ◽  
Neoplastic Cell ◽  
Computational Approach ◽  
Pancreatic Tumors ◽  
Published Data ◽  
Cell Type ◽  
Disease Heterogeneity ◽  
Manual Task ◽  
Insight Into

Abstract Single cell transcriptomics is revolutionising our understanding of tissue and disease heterogeneity, yet cell type identification remains a partially manual task. Published algorithms for automatic cell annotation are limited to known cell types and fail to capture novel populations, especially cancer cells. We developed northstar, a computational approach to classify thousands of cells based on published data within seconds while simultaneously identifying and highlighting new cell states such as malignancies. We tested northstar on data from glioblastoma, melanoma, and seven different healthy tissues and obtained high accuracy and robustness. We collected eleven pancreatic tumors and identified three shared and five private neoplastic cell populations, offering insight into the origins of neuroendocrine and exocrine tumors. Northstar is a useful tool to assign known and novel cell type and states in the age of cell atlases.

scholarly journals Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Julien Racle ◽  
Kaat de Jonge ◽  
Petra Baumgaertner ◽  
Daniel E Speiser ◽  
David Gfeller
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Response To Therapy ◽  
Expression Data ◽  
Cell Type ◽  
Tumor Gene Expression ◽  
Tumor Gene

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

scholarly journals Cell-based therapy technology classifications and translational challenges

2015 ◽  
Vol 370 (1680) ◽  
pp. 20150017 ◽  
Author(s):  
Natalie M. Mount ◽  
Stephen J. Ward ◽  
Panos Kefalas ◽  
Johan Hyllner
Keyword(s):  
Cell Types ◽  
Clinical Translation ◽  
Diverse Group ◽  
Patient Access ◽  
Cell Type ◽  
Commercial Development ◽  
Cell Therapies ◽  
Cell Based Therapy ◽  
Technology Area

Cell therapies offer the promise of treating and altering the course of diseases which cannot be addressed adequately by existing pharmaceuticals. Cell therapies are a diverse group across cell types and therapeutic indications and have been an active area of research for many years but are now strongly emerging through translation and towards successful commercial development and patient access. In this article, we present a description of a classification of cell therapies on the basis of their underlying technologies rather than the more commonly used classification by cell type because the regulatory path and manufacturing solutions are often similar within a technology area due to the nature of the methods used. We analyse the progress of new cell therapies towards clinical translation, examine how they are addressing the clinical, regulatory, manufacturing and reimbursement requirements, describe some of the remaining challenges and provide perspectives on how the field may progress for the future.

scholarly journals EnClaSC: A novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes

2019 ◽  
Author(s):  
Xiaoyang Chen ◽  
Shengquan Chen ◽  
Rui Jiang
Keyword(s):  
Single Cell ◽  
Rapid Development ◽  
Scale Up ◽  
Cell Types ◽  
Cell Type ◽  
Species Classification ◽  
Transcriptomic Data ◽  
General Data ◽  
Type Classification

AbstractBackgroundIn recent years, the rapid development of single-cell RNA-sequencing (scRNA-seq) techniques enables the quantitative characterization of cell types at a single-cell resolution. With the explosive growth of the number of cells profiled in individual scRNA-seq experiments, there is a demand for novel computational methods for classifying newly-generated scRNA-seq data onto annotated labels. Although several methods have recently been proposed for the cell-type classification of single-cell transcriptomic data, such limitations as inadequate accuracy, inferior robustness, and low stability greatly limit their wide applications.ResultsWe propose a novel ensemble approach, named EnClaSC, for accurate and robust cell-type classification of single-cell transcriptomic data. Through comprehensive validation experiments, we demonstrate that EnClaSC can not only be applied to the self-projection within a specific dataset and the cell-type classification across different datasets, but also scale up well to various data dimensionality and different data sparsity. We further illustrate the ability of EnClaSC to effectively make cross-species classification, which may shed light on the studies in correlation of different species. EnClaSC is freely available at https://github.com/xy-chen16/EnClaSC.ConclusionsEnClaSC enables highly accurate and robust cell-type classification of single-cell transcriptomic data via an ensemble learning method. We expect to see wide applications of our method to not only transcriptome studies, but also the classification of more general data.

scholarly journals Fibroblasts – the neglected cell type in peripheral sensitization and chronic pain? - A systematic view on the current state of the literature

2021 ◽  
Author(s):  
Naomi Shinotsuka ◽  
Franziska Denk
Keyword(s):  
Chronic Pain ◽  
Cell Function ◽  
Immune Cell ◽  
Data Extraction ◽  
Cell Types ◽  
Extraction Methods ◽  
Peripheral Sensitization ◽  
Cell Type ◽  
Systematic Screening ◽  
Pain Research

AbstractChronic pain and its underlying biological mechanisms have been studied for many decades, with a myriad of molecules, receptors and cell types known to contribute to abnormal pain sensations. We now know that besides an obvious role for neuronal populations in the peripheral and central nervous system, immune cells like microglia, macrophages and T cells are also important drivers of persistent pain. While neuroinflammation has therefore been widely studied in pain research, there is one cell-type that appears to be rather neglected in this context: the humble fibroblast.Fibroblasts may seem unassuming, but actually play a major part in regulating immune cell function and driving chronic inflammation. What is known about them in the context chronic pain?Here we set out to analyze the literature on this topic – using systematic screening and data extraction methods to obtain a balanced view on what has been published. We found that there has been surprisingly little research in this area: 134 articles met our inclusion criteria, only a tiny minority of which directly investigated interactions between fibroblasts and peripheral neurons. We categorized the articles we included – stratifying them according to what was investigated, the estimated quality of results, and any common conclusions.Fibroblasts are a ubiquitous cell type and a prominent source of many pro-algesic mediators in a wide variety of tissues. We think that they deserve a more central role in pain research and propose a new, testable model of how fibroblasts might drive peripheral neuron sensitization.

scholarly journals Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system

2021 ◽  
Author(s):  
Congmin Xu ◽  
Junkai Yang ◽  
Astrid Kosters ◽  
Benjamin R Babcock ◽  
Peng Qiu ◽  
...  
Keyword(s):  
Immune System ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Cell Receptor ◽  
Surface Proteins ◽  
Cell Type ◽  
Human Immune System ◽  
Receptor Repertoire ◽  
Human Immune

Single-cell transcriptomics enables the definition of diverse human immune cell types across multiple tissue and disease contexts. Still, deeper biological understanding requires comprehensive integration of multiple single-cell omics (transcriptomic, proteomic, and cell receptor repertoire). To improve the identification of diverse cell types and the accuracy of cell-type classification in our multi-omics single-cell datasets, we developed SuPERR-seq, a novel analysis workflow to increase the resolution and accuracy of clustering and allow for the discovery and characterization of previously hidden cell subsets. We show that by incorporating information from cell-surface proteins and immunoglobulin transcript counts, we accurately remove cell doublets and prevent widespread cell-type misclassification. This approach uniquely improves the identification of heterogeneous cell types in the human immune system, including a novel subset of antibody-secreting cells in the bone marrow.

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