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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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Systems Biochemistry Approaches to Defining Mitochondrial Protein Function

TL;DR: Recent systems biochemistry approaches have accelerated the identification of new disease-related mitochondrial proteins and of long-sought "missing" proteins that fulfill key functions, moving us toward a more complete understanding of mitochondrial activities and providing a molecular framework for the investigation of mitochondrial pathogenesis.
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Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data

TL;DR: AutoCell as discussed by the authors is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data.
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Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software

TL;DR: ASHLAR as mentioned in this paper coordinates stitching and registration and scales to 103 or more image tiles over many imaging cycles to generate accurate, high-plex image mosaics, the key type of data for downstream visualization and computational analysis.
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The cell type composition of the adult mouse brain revealed by single cell and spatial genomics

TL;DR: In this article , the authors constructed a comprehensive atlas of cell types in each brain structure, paired high-throughput single-nucleus RNA-seq with Slide-seq-a recently developed spatial transcriptomics method with near-cellular resolution.
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Alignment of Cell Lineage Trees Elucidates Genetic Programs for the Development and Evolution of Cell Types.

TL;DR: Quantified phenotypic similarity between CLTs using a novel computational method that exhaustively searches for optimal correspondence between individual cells meanwhile retaining their topological relationships to help answer the myriad of questions surrounding the developmental process.
References
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Journal ArticleDOI

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.
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Fast unfolding of communities in large networks

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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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