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Open AccessJournal ArticleDOI

The Human Cell Atlas

Aviv Regev, +81 more
- 05 Dec 2017 - 
- Vol. 6
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TLDR
An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease.
Abstract
The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

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Citations
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Journal ArticleDOI

Systems Immunology: Learning the Rules of the Immune System

TL;DR: This work focuses on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts the authors' ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors.
Posted ContentDOI

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

TL;DR: A robust statistical model, scvis, is presented, to capture and visualize the low-dimensional structures in single-cell gene expression data and is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding.
Journal ArticleDOI

Advances and Opportunities in Single-Cell Transcriptomics for Plant Research

TL;DR: The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications as mentioned in this paper, which is changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell.
Posted ContentDOI

Harmonization and Annotation of Single-cell Transcriptomics data with Deep Generative Models

TL;DR: It is demonstrated that scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as a case study.
Posted ContentDOI

High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes

TL;DR: This work uses Repertoire And Gene Expression sequencing (RAGE-seq) to accurately characterize full-length T cell (TCR) and B cell (BCR) receptor sequences and transcriptional profiles of more than 7,138 lymphocytes sampled from the primary tumour and draining lymph node of a breast cancer patient.
References
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Continuous cultures of fused cells secreting antibody of predefined specificity

TL;DR: The derivation of a number of tissue culture cell lines which secrete anti-sheep red blood cell (SRBC) antibodies is described here, made by fusion of a mouse myeloma and mouse spleen cells from an immunised donor.
Journal ArticleDOI

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Journal ArticleDOI

Fast unfolding of communities in large networks

TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.
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