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

2019 ◽  
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

AbstractSingle cell transcriptomics is revolutionising our understanding of tissue and disease heterogeneity, yet cell type identificationl 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 human glioblastoma and melanoma 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 Panoramic stitching of heterogeneous single-cell transcriptomic data

2018 ◽  
Author(s):  
Brian Hie ◽  
Bryan Bryson ◽  
Bonnie Berger
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Data Sets ◽  
Cell Type ◽  
Data Set ◽  
Wide Range ◽  
Data Set Integration ◽  
Biological Patterns ◽  
Insight Into ◽  
Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

scholarly journals Ensemble learning for classifying single-cell data and projection across reference atlases

2020 ◽  
Vol 36 (11) ◽  
pp. 3585-3587
Author(s):  
Lin Wang ◽  
Francisca Catalan ◽  
Karin Shamardani ◽  
Husam Babikir ◽  
Aaron Diaz
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Status Quo ◽  
Supplementary Information ◽  
Published Data ◽  
Supplementary Data ◽  
Cell Type ◽  
Low Sensitivity ◽  
Project Data ◽  
Cell Data

Abstract Summary Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms. Availability and implementation https://github.com/diazlab/ELSA Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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 Super interactive promoters provide insight into cell type-specific regulatory networks in blood lineage cell types

2021 ◽  
Author(s):  
Taylor M. Lagler ◽  
Yuchen Yang ◽  
Yuriko Harigaya ◽  
Vijay G. Sankaran ◽  
Ming Hu ◽  
...  
Keyword(s):  
Blood Cell ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Spatial Organization ◽  
Cell Types ◽  
Gene Promoters ◽  
Cell Type ◽  
A Cell ◽  
Cell Type Specific ◽  
Insight Into

Existing studies of chromatin conformation have primarily focused on potential enhancers interacting with gene promoters. By contrast, the interactivity of promoters per se, while equally critical to understanding transcriptional control, has been largely unexplored, particularly in a cell type-specific manner for blood lineage cell types. In this study, we leverage promoter capture Hi-C data across a compendium of blood lineage cell types to identify and characterize cell type-specific super-interactive promoters (SIPs). Notably, promoter-interacting regions (PIRs) of SIPs are more likely to overlap with cell type-specific ATAC-seq peaks and GWAS variants for relevant blood cell traits than PIRs of non-SIPs. Further, SIP genes tend to express at a higher level in the corresponding cell type, and show enriched heritability of relevant blood cell trait(s). Importantly, this analysis shows the potential of using promoter-centric analyses of chromatin spatial organization data to identify biologically important genes and their regulatory regions.

332. GYCOMIC ANALYSES OF THE GRANULOSA AND THECA OF BOVINE OVARIAN FOLLICLES

2010 ◽  
Vol 22 (9) ◽  
pp. 132
Author(s):  
N. Hatzirodos ◽  
J. Nigro ◽  
A. V. Vashi ◽  
H. F. Irving-Rodgers ◽  
B. Caterson ◽  
...  
Keyword(s):  
Granulosa Cells ◽  
Cell Function ◽  
Chondroitin Sulphate ◽  
Heparan Sulphate ◽  
Cell Types ◽  
Ovarian Follicles ◽  
Cell Type ◽  
Thecal Cells ◽  
Theca Externa

Development of ovarian follicles involves changes in cell function and remodelling of the follicular wall. Remodelling necessitates changes in the extracellular matrix, including proteoglycans (PGs). PGs contain glycosaminoglycans (GAGs) covalently bound to a protein core. The length of GAG chains in PGs and the degree and pattern of sulphation differs between cell types and change as cells alter their phenotype. PGs that have been identified in follicles include the chondroitin sulphate (CS) PG, versican and inter-a trypsin inhibitor, and the heparan sulphate (HS) PGs, perlecan and type XVIII collagen. The latter two are found in focimatrix, the follicular and the thecal subendothelial basal laminas. To examine GAGs composition in follicles, bovine antral follicles of various sizes were collected. Follicles were dissected and a biopsy taken for histological classification of health. Theca layers and granulosa cells were collected separately and analysed by fluorophore-assisted carbohydrate (FACE) analysis of GAGs following digestion to disaccharides with chondroitinase ABC, hyaluronidase, heparinase, and heparitinases I and II. Four non GAG sugars and 12 different GAG derived disaccharides were identified and quantitated on a per DNA basis. Healthy versus atretic follicles for each cell type were compared and correlation analyses were also undertaken. Immunohistochemistry using CS specific antibodies was also conducted. There was no effect of size on the GAG content for either granulosa or theca cells. The 4- and 6- sulphated CS sugars were the most abundant following digestion in all tissues. Theca had higher levels than granulosa cells of HS derived disaccharides and also of un- or 4- or 6- N-sulphated CS derived disaccharides. Some sulphated CS moieties localised uniquely to the stroma surrounding blood vessels in the theca externa. Atretic follicles had lower amounts of disaccharides derived from HS in both granulosa and thecal cells, suggesting that heparanase may be activated upon atresia.

scholarly journals Structure and mechanism of SARS-CoV-2 Spike N679-V687 deletion variant elucidate cell-type specific evolution of viral fitness

2021 ◽  
Author(s):  
Kapil Gupta ◽  
Christine Toelzer ◽  
Maia Kavanagh Williamson ◽  
Deborah Shoemark ◽  
A. Sofia F. Oliveira ◽  
...  
Keyword(s):  
Cell Types ◽  
Viral Fitness ◽  
Cell Tropism ◽  
Cell Type ◽  
Amino Acid Deletion ◽  
Recognition Motif ◽  
Mechanistic Insight ◽  
Distinct Cell ◽  
Cell Type Specific ◽  
Insight Into

As the global burden of SARS-CoV-2 infections escalates, so does the evolution of viral variants which is of particular concern due to their potential for increased transmissibility and pathology. In addition to this entrenched variant diversity in circulation, RNA viruses can also display genetic diversity within single infected hosts with co-existing viral variants evolving differently in distinct cell types. The BriSΔ variant, originally identified as a viral subpopulation by passaging SARS-CoV-2 isolate hCoV-19/England/02/2020, comprises in the spike glycoprotein an eight amino-acid deletion encompassing the furin recognition motif and S1/S2 cleavage site. Here, we analyzed the structure, function and molecular dynamics of this variant spike, providing mechanistic insight into how the deletion correlates to viral cell tropism, ACE2 receptor binding and infectivity, allowing the virus to probe diverse trajectories in distinct cell types to evolve viral fitness.

scholarly journals A Single Cell Transcriptomic Atlas Characterizes Aging Tissues in the Mouse

2019 ◽  
Author(s):  
◽  
Angela Oliveira Pisco ◽  
Aaron McGeever ◽  
Nicholas Schaum ◽  
Jim Karkanias ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Cellular Processes ◽  
Progressive Loss ◽  
Age Related ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Molecular Information ◽  
Insight Into

AbstractAging is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death1. Despite rapid advances over recent years, many of the molecular and cellular processes which underlie progressive loss of healthy physiology are poorly understood2. To gain a better insight into these processes we have created a single cell transcriptomic atlas across the life span of Mus musculus which includes data from 23 tissues and organs. We discovered cell-specific changes occurring across multiple cell types and organs, as well as age related changes in the cellular composition of different organs. Using single-cell transcriptomic data we were able to assess cell type specific manifestations of different hallmarks of aging, such as senescence3, genomic instability4 and changes in the organism’s immune system2. This Tabula Muris Senis provides a wealth of new molecular information about how the most significant hallmarks of aging are reflected in a broad range of tissues and cell types.

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

2020 ◽  
Vol 21 (S13) ◽  
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

Abstract Background In 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. Results We 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. Conclusions EnClaSC 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.

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