scispace - formally typeset
Open AccessJournal ArticleDOI

The Human Cell Atlas

Aviv Regev, +81 more
- 05 Dec 2017 - 
- Vol. 6
Reads0
Chats0
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.

read more

Citations
More filters
Journal ArticleDOI

Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis.

TL;DR: How the computational tools available to reconstruct lineage trajectories, quantify cell fate bias, and perform dimensionality re- duction for data visualization are providing new mechanistic insights into the process of cell fate decision is discussed.
Journal ArticleDOI

Expansion microscopy: enabling single cell analysis in intact biological systems

TL;DR: Expansion microscopy is a technology that physically magnifies tissues in an isotropic way, thereby achieving super‐resolution microscopy on diffraction‐limited microscopes, enabling rapid image acquisition and large field of view.
Posted ContentDOI

Wiring together large single-cell RNA-seq sample collections

TL;DR: A flexible approach, called Conos (Clustering On Network Of Samples), that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells, which enables investigators to balance between resolution and breadth of the detected subpopulations.
Journal ArticleDOI

TSEA-DB: a trait-tissue association map for human complex traits and diseases.

TL;DR: A reference database for trait-associated tissue specificity based on genome-wide association study (GWAS) results, named Tissue-Specific Enrichment Analysis DataBase (TSEA-DB), which aims to provide reference tissue(s) enriched with the genes from GWAS.
Journal ArticleDOI

Probe-Seq enables transcriptional profiling of specific cell types from heterogeneous tissue by RNA-based isolation.

TL;DR: Probe-Seq is developed, which allows deep transcriptional profiling of specific cell types isolated using RNA as the defining feature, and is compatible with frozen nuclei, making cell types within archival tissue immediately accessible.
References
More filters
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.
Journal ArticleDOI

Fast unfolding of communities in large networks

TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
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.
Related Papers (5)