Literature DB >> 23998189

Single-cell analysis: toward the clinic.

Michael R Speicher1.   

Abstract

Entities:  

Year:  2013        PMID: 23998189      PMCID: PMC3978650          DOI: 10.1186/gm478

Source DB:  PubMed          Journal:  Genome Med        ISSN: 1756-994X            Impact factor:   11.117


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Human life starts with a single cell, the zygote, which undergoes multiple mitotic cell divisions to form a complete body. During each cell division, the genome has to be replicated to form the approximately 1014 cells that make up the adult human body. For a long time, it was thought that all cells from the same individual harbored identical genomes. The only known exceptions being lymphocytes, in which antigen- and pathogen-driven recombination and hypermutation generate genetic diversity, and germline cells, which undergo meiotic recombination. During normal mitotic cell division, however, the genome is not replicated with absolute precision; this results in the incorporation of somatic mutations [1]. Estimates that are based on known mutation rates suggest that every cell division creates some form of genetic variation, which may or may not have an effect on cellular function [2,3]. During the progression from the zygotic stage to adulthood, these somatic mutations accumulate (Figure 1). Recent genome-wide assays have suggested that an individual does not have one genome, but is instead made up of a population of different cells. This situation is referred to as mosaicism, that is, the co-occurrence of several cell lineages with different genotypes in one individual who has developed from a single fertilized egg. The true extent of such mosaicism is unknown, but its presence appears to be the rule rather than the exception [4]. Estimates of the mutation burden in somatic cells are high and, as a consequence, it has been speculated that each cell in a human body may have a unique genomic signature; in other words, that each cell has its own 'personal genome' [4,5].
Figure 1

A schematic representation of the effects of somatic mutations at different phases of development and tissue renewal. Life starts from a single cell, a fertilized egg (blue circle). A complete organism, that is a human, is formed from this cell by many cell divisions. Novel somatic mutations can occur with each cell division. The diagram shows how such mutations are passed on to daughter cells as the organism develops: a mutation may undergo clonal expansion during tissue renewal. If the somatic mutation occurs late (brown clones), the mutation will be found in only a small compartment of the body, that is, it is likely to be confined to one organ. If the mutation occurs very early in development - for example, during embryogenesis (dark blue clones) - it is likely to occur in different organs. Successive mutations, which can then establish organismal cell lineage trees, can occur in cells derived from those that underwent an early mutation (clones in lighter blue color within the dark blue clone). The serial acquisition of novel mutations is shown as an example for the first series of blue clones (red bases). Some mutations may be disadvantageous and go extinct (black clones). (Image adapted in part from [6].)

A schematic representation of the effects of somatic mutations at different phases of development and tissue renewal. Life starts from a single cell, a fertilized egg (blue circle). A complete organism, that is a human, is formed from this cell by many cell divisions. Novel somatic mutations can occur with each cell division. The diagram shows how such mutations are passed on to daughter cells as the organism develops: a mutation may undergo clonal expansion during tissue renewal. If the somatic mutation occurs late (brown clones), the mutation will be found in only a small compartment of the body, that is, it is likely to be confined to one organ. If the mutation occurs very early in development - for example, during embryogenesis (dark blue clones) - it is likely to occur in different organs. Successive mutations, which can then establish organismal cell lineage trees, can occur in cells derived from those that underwent an early mutation (clones in lighter blue color within the dark blue clone). The serial acquisition of novel mutations is shown as an example for the first series of blue clones (red bases). Some mutations may be disadvantageous and go extinct (black clones). (Image adapted in part from [6].) Many of these mutations will be neutral, in other words, they do not result in a selective advantage or disadvantage for the cell (Figure 1). On the other hand, some mutations may affect genes, and subsequently transcription and protein synthesis. The exact range of the possible biological functions of such somatic mutations is hard to grasp. Some somatic mutations are instrumental in causing diseases, especially cancers [6], or for the physiologic process of aging [7]. The apparently large extent of somatic mosaicism suggests, however, that some mutations may have normal physiological functions [4]. For example, the brain appears to harbor widespread somatic mutations in the form of aneuploidy or retrotransposon insertions, and it has been speculated that this extensive somatic genome mosaicism might contribute to normal brain function [8,9]. The extent of genetic mosaicism has tremendous implications for both basic research and clinical applications. Despite Virchow's discovery more than 150 years ago that the single cell represents the basic unit of disease [10], research and diagnostics are usually performed on thousands of cells without considering the different cell lineages in a body. Such a diagnostic provides only average information about the cells examined. The usual source of cells for clinical diagnostics is the peripheral blood because of its easy accessibility. In cases of suspected mosaicism, the usual diagnostics include additional analyses of non-hematogenous cells, which are obtained by skin biopsies and/or buccal swabs, or the analyses of paired samples of visibly affected and normal tissue. The latter strategy resulted, for example, in the identification of somatic AKT1 mutations as the cause of Proteus syndrome [11] and of somatic GNAQ mutations in individuals with Sturge-Weber syndrome [12]. In the case of unaffected parents who have an affected child, human geneticists have to consider the option that one parent has a germline mosaic, which would affect the recurrence risk for the affected child's sibling. Nevertheless, germline mosaics are rarely analyzed further as germ cells are difficult to obtain from females. To understand a complex phenotype, large-scale, whole-body single-cell-type analyses, including the characterization of genomics, transcriptomics, proteomics, and epigenomics are needed. Such analyses would greatly contribute to an improvement of our fundamental understanding of both biology and medicine. Furthermore, they would most probably reveal multiple novel insights into disease occurrence and aging. How could such analyses of single-cells from various tissue types be performed? One suggestion is a more detailed analysis of all surgically excised tissues, including tonsils, appendices, defective heart valves, skeletal muscle, and normal tissue in the proximity of tumors [4]. If this material were obtained in a viable form, somatic cells could be reprogrammed in order to generate induced pluripotent stem cells (iPSCs). For example, widespread somatic mosaicism that results from acquired post-zygotic structural alterations in human skin has recently been detected by whole-genome and transcriptome analysis of iPSCs derived from primary skin fibroblasts [13]. The derivation of iPSCs is attractive as it offers the opportunity to examine single cells at many levels - genomics, proteomics, transcriptomics, metabolomics and systems biology - at high resolution and sensitivity. In addition to iPSCs, multiple novel single-cell techniques have emerged in recent years, making the genome, transcriptome and proteome of single cells accessible to detailed analyses. Many of these novel methods are discussed in various contributions in this series of articles for Genome Medicine. With the current progress in developing sophisticated single-cell approaches, what biological and medical questions can be addressed? First, single-cell analyses will improve our understanding of intercellular variability and its biological consequences in connection with disease susceptibility and aging. Second, single-cell analyses might contribute to a better definition of cell types. At present, the classification of cell types is based on characteristics, such as morphology, genotype, phenotype, or developmental origin. There is no common agreement on what really defines a cell type [5]. Hence, large-scale single-cell transcriptome or epigenome analyses might result in an improved definition of cell types and could also help to identify rare cell types [5]. Third, the strength of single-cell technologies lies in their ability to analyze rare cell events. For example, for patients with cancer, single-cell technologies are playing an increasing role in the detection of minimal residual disease or in the analysis of circulating tumor cells in the peripheral blood. Fourth, single-cell technologies are already instrumental in pre-implantation diagnosis, where just one or two cells from the blastocyst are commonly subjected to analysis. Finally, single-cell technologies will contribute to unraveling the true extent of single-cell somatic mutations. This will make it possible to use the accumulation of mutations in single cells during development to infer the lineage ancestry of each cell (Figure 1), which will answer important questions in human biology and medicine [5]. At present, the clinical use of single-cell analysis is - with the exception of pre-implantation diagnosis - still in its infancy. For the reasons mentioned above, however, single-cell diagnostics will be instrumental for the realization of personalized medicine and for the development of completely novel therapeutic concepts. Hence, the bold prediction has been made that we are facing an era of integrated single-cell genomic, epigenomic, transcriptomic, and proteomic analysis that will revolutionize whole-organism science [5].

Abbreviation

iPSC: induced pluripotent stem cell.

Competing interests

The author declares that they have no competing interests.
  12 in total

Review 1.  Single-cell sequencing-based technologies will revolutionize whole-organism science.

Authors:  Ehud Shapiro; Tamir Biezuner; Sten Linnarsson
Journal:  Nat Rev Genet       Date:  2013-07-30       Impact factor: 53.242

2.  Sturge-Weber syndrome and port-wine stains caused by somatic mutation in GNAQ.

Authors:  Matthew D Shirley; Hao Tang; Carol J Gallione; Joseph D Baugher; Laurence P Frelin; Bernard Cohen; Paula E North; Douglas A Marchuk; Anne M Comi; Jonathan Pevsner
Journal:  N Engl J Med       Date:  2013-05-08       Impact factor: 91.245

3.  Evolution of the mutation rate.

Authors:  Michael Lynch
Journal:  Trends Genet       Date:  2010-06-30       Impact factor: 11.639

Review 4.  Somatic mutation, genomic variation, and neurological disease.

Authors:  Annapurna Poduri; Gilad D Evrony; Xuyu Cai; Christopher A Walsh
Journal:  Science       Date:  2013-07-05       Impact factor: 47.728

5.  A mosaic activating mutation in AKT1 associated with the Proteus syndrome.

Authors:  Marjorie J Lindhurst; Julie C Sapp; Jamie K Teer; Jennifer J Johnston; Erin M Finn; Kathryn Peters; Joyce Turner; Jennifer L Cannons; David Bick; Laurel Blakemore; Catherine Blumhorst; Knut Brockmann; Peter Calder; Natasha Cherman; Matthew A Deardorff; David B Everman; Gretchen Golas; Robert M Greenstein; B Maya Kato; Kim M Keppler-Noreuil; Sergei A Kuznetsov; Richard T Miyamoto; Kurt Newman; David Ng; Kevin O'Brien; Steven Rothenberg; Douglas J Schwartzentruber; Virender Singhal; Roberto Tirabosco; Joseph Upton; Shlomo Wientroub; Elaine H Zackai; Kimberly Hoag; Tracey Whitewood-Neal; Pamela G Robey; Pamela L Schwartzberg; Thomas N Darling; Laura L Tosi; James C Mullikin; Leslie G Biesecker
Journal:  N Engl J Med       Date:  2011-07-27       Impact factor: 91.245

6.  Half or more of the somatic mutations in cancers of self-renewing tissues originate prior to tumor initiation.

Authors:  Cristian Tomasetti; Bert Vogelstein; Giovanni Parmigiani
Journal:  Proc Natl Acad Sci U S A       Date:  2013-01-23       Impact factor: 11.205

Review 7.  The hallmarks of aging.

Authors:  Carlos López-Otín; Maria A Blasco; Linda Partridge; Manuel Serrano; Guido Kroemer
Journal:  Cell       Date:  2013-06-06       Impact factor: 41.582

8.  Somatic copy number mosaicism in human skin revealed by induced pluripotent stem cells.

Authors:  Alexej Abyzov; Jessica Mariani; Dean Palejev; Ying Zhang; Michael Seamus Haney; Livia Tomasini; Anthony F Ferrandino; Lior A Rosenberg Belmaker; Anna Szekely; Michael Wilson; Arif Kocabas; Nathaniel E Calixto; Elena L Grigorenko; Anita Huttner; Katarzyna Chawarska; Sherman Weissman; Alexander Eckehart Urban; Mark Gerstein; Flora M Vaccarino
Journal:  Nature       Date:  2012-11-18       Impact factor: 49.962

9.  Somatic retrotransposition alters the genetic landscape of the human brain.

Authors:  J Kenneth Baillie; Mark W Barnett; Kyle R Upton; Daniel J Gerhardt; Todd A Richmond; Fioravante De Sapio; Paul M Brennan; Patrizia Rizzu; Sarah Smith; Mark Fell; Richard T Talbot; Stefano Gustincich; Thomas C Freeman; John S Mattick; David A Hume; Peter Heutink; Piero Carninci; Jeffrey A Jeddeloh; Geoffrey J Faulkner
Journal:  Nature       Date:  2011-10-30       Impact factor: 49.962

10.  Genomic variability within an organism exposes its cell lineage tree.

Authors:  Dan Frumkin; Adam Wasserstrom; Shai Kaplan; Uriel Feige; Ehud Shapiro
Journal:  PLoS Comput Biol       Date:  2005-10-28       Impact factor: 4.475

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1.  Concurrent whole-genome haplotyping and copy-number profiling of single cells.

Authors:  Masoud Zamani Esteki; Eftychia Dimitriadou; Ligia Mateiu; Cindy Melotte; Niels Van der Aa; Parveen Kumar; Rakhi Das; Koen Theunis; Jiqiu Cheng; Eric Legius; Yves Moreau; Sophie Debrock; Thomas D'Hooghe; Pieter Verdyck; Martine De Rycke; Karen Sermon; Joris R Vermeesch; Thierry Voet
Journal:  Am J Hum Genet       Date:  2015-05-14       Impact factor: 11.025

Review 2.  Next-generation molecular diagnosis: single-cell sequencing from bench to bedside.

Authors:  Wanjun Zhu; Xiao-Yan Zhang; Sadie L Marjani; Jialing Zhang; Wengeng Zhang; Shixiu Wu; Xinghua Pan
Journal:  Cell Mol Life Sci       Date:  2016-10-13       Impact factor: 9.261

3.  Isolation and Characterization of Single Cells from Zebrafish Embryos.

Authors:  Leigh Ann Samsa; Nicole Fleming; Scott Magness; Li Qian; Jiandong Liu
Journal:  J Vis Exp       Date:  2016-03-12       Impact factor: 1.355

4.  Single-cell transcriptome in the identification of disease biomarkers: opportunities and challenges.

Authors:  Zhitu Zhu; Diane C Wang; Laurenţiu M Popescu; Xiangdong Wang
Journal:  J Transl Med       Date:  2014-08-12       Impact factor: 5.531

5.  acdc - Automated Contamination Detection and Confidence estimation for single-cell genome data.

Authors:  Markus Lux; Jan Krüger; Christian Rinke; Irena Maus; Andreas Schlüter; Tanja Woyke; Alexander Sczyrba; Barbara Hammer
Journal:  BMC Bioinformatics       Date:  2016-12-20       Impact factor: 3.169

Review 6.  Current challenges in the bioinformatics of single cell genomics.

Authors:  Luwen Ning; Geng Liu; Guibo Li; Yong Hou; Yin Tong; Jiankui He
Journal:  Front Oncol       Date:  2014-01-27       Impact factor: 6.244

7.  Single Cell Analysis: From Technology to Biology and Medicine.

Authors:  Xinghua Pan
Journal:  Single Cell Biol       Date:  2014

8.  Feasibility of a workflow for the molecular characterization of single cells by next generation sequencing.

Authors:  Francesca Salvianti; Giada Rotunno; Francesca Galardi; Francesca De Luca; Marta Pestrin; Alessandro Maria Vannucchi; Angelo Di Leo; Mario Pazzagli; Pamela Pinzani
Journal:  Biomol Detect Quantif       Date:  2015-09-12

9.  Mutational analysis of single circulating tumor cells by next generation sequencing in metastatic breast cancer.

Authors:  Francesca De Luca; Giada Rotunno; Francesca Salvianti; Francesca Galardi; Marta Pestrin; Stefano Gabellini; Lisa Simi; Irene Mancini; Alessandro Maria Vannucchi; Mario Pazzagli; Angelo Di Leo; Pamela Pinzani
Journal:  Oncotarget       Date:  2016-05-03

10.  Transcription factor network analysis based on single cell RNA-seq identifies that Trichostatin-a reverses docetaxel resistance in prostate Cancer.

Authors:  Patricia M Schnepp; Aqila Ahmed; June Escara-Wilke; Jinlu Dai; Greg Shelley; Jill Keller; Atsushi Mizokami; Evan T Keller
Journal:  BMC Cancer       Date:  2021-12-08       Impact factor: 4.430

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