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Single-cell genome-wide studies give new insight into nongenetic cell-to-cell variability in animals

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Abstract

Huge numbers of cells form an adult animal body, ranging from several thousands in Placozoa and small nematodes to many billions in mammals. Cells are classified into separate groups known as cell types by their morphological and biochemical features. Six to several hundreds of spatially ordered cell types are recognized in different animals. This complex organization develops from one cell, a zygote, during ontogeny, and its dynamic equilibrium is often maintained in the adult body. One of the key challenges in biology is to understand the mechanisms that sustain the reproducible development of a complex ordered cell ensemble such as the animal body from a single cell. How cells with identical genomes stably maintain one of the numerous possible phenotypes? How the cell differentiation lineage is selected during development? What genes play a key role in maintaining cell identity, and how do they determine expression of other genes characteristic of the relevant cell type? How does the basically stochastic nature of transcription in an isolated cell affect the stability of cell identity, the selection of a cell lineage, and the variability of cell responses to external stimuli? Better-grounded answers to these questions have become possible with recent progress in single-cell genome-wide analysis techniques, which combine the high throughput of biochemical methods and the differential nature of microscopy. The techniques are still in their infancy, and their further development will certainly revolutionize many fields of life sciences and, in particular, developmental biology. Here, we summarize the main results that have been obtained in single-cell genome-wide analyses and describe the nongenetic cell-to-cell variability in animals.

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Abbreviations

aRME:

Autosomal random monoallelic expression

AT1 and AT2 cells:

Type 1 and type 2 alveolar cells

ATAC-seq:

Assay for transposase-accessible chromatin with sequencing

BMDCs:

Bone marrow-derived dendritic cells

BS-seq:

Bisulfite sequencing

CAP:

Circular a posteriori projection

ChIA-PET:

Chromatin interaction analysis by paired-end tag sequencing

ChIP-seq:

Chromatin immunoprecipitation and sequencing

DamID:

DNA adenine methyltransferase identification

EM algorithm:

Expectation–maximization algorithm

FACS:

Fluorescence-activated cell sorting

FISH:

Fluorescence in situ hybridization

FISSEQ:

Fluorescent in situ sequencing

GRN:

Gene regulatory network

hESC:

Human embryonic stem cell

iPS:

Induced pluripotent stem cell

LAD:

Lamina-associated domain

LADR:

lncRNA activated during reprogramming

LMR:

Low methylation region

lncRNA:

Long noncoding RNA

LPS:

Lipopolysaccharides

MALDI:

Matrix-assisted laser desorption/ionization

MeDIP-seq:

Methylated DNA immunoprecipitation and sequencing

mESC:

Mouse embryonic stem cell

NSC:

Neural stem cell

PRC-2:

polycomb repressive complex 2

Race ID:

Rare cell type identification algorithm

SC:

Single-cell

SC-seq:

Single-cell sequencing based

TAD:

Topologically associating domain

t-SNE:

t-distributed stochastic neighbor embedding

Th2:

Type 2 helper T cells

TIVA:

Transcriptome in vivo analysis

UMI:

Unique molecular identifier

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Acknowledgments

This work was supported by the Russian Science Foundation (project #14-14-01088).

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  1. Institute of Gene Biology of the Russian Academy of Sciences, Vavilov Street 34/5, Moscow, Russia, 119334

    Arkadiy K. Golov, Sergey V. Razin & Alexey A. Gavrilov

  2. Molecular Biology Department, Biological Faculty, Lomonosov Moscow State University, Moscow, Russia, 119992

    Sergey V. Razin

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Golov, A.K., Razin, S.V. & Gavrilov, A.A. Single-cell genome-wide studies give new insight into nongenetic cell-to-cell variability in animals. Histochem Cell Biol 146, 239–254 (2016). https://doi.org/10.1007/s00418-016-1466-z

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