FISHing for Unstable Cellular Genomes in the Human Brain
1. Mental Health Research Center, Moscow, Russia
2. Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Moscow, Russia
3. Department of medical genetics, Russian Medical Academy of Continuous Professional Education, Moscow, Russia
Academic Editor: Thomas Liehr
Special Issue: Applications of Fluorescence in Situ Hybridization
Received: February 06, 2019 | Accepted: April 21, 2019 | Published: April 30, 2019
OBM Genetics 2019, Volume 3, Issue 2 doi:10.21926/obm.genet.1902076
Recommended citation: Yurov YB, Vorsanova SG, Iourov IY. FISHing for Unstable Cellular Genomes in the Human Brain. OBM Genetics 2019;3(2):11; doi:10.21926/obm.genet.1902076.
© 2019 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.
The studies of chromosomes within the central nervous system have long been recognized as an important part of neuroscience and genetic research [1,2]. A growing body of research on chromosomal variations in the human brain forms the theoretical and empirical basis of the emerging field of molecular neurocytogenetics . Currently, intercellular genome variations at the chromosomal level are generally accepted to be a causative mechanism for brain diseases and neuronal diversity [4,5]. Taking into account the immense diversity of neuronal cells populating the human brain , it is not surprising that somatic mosaicism has become the focus of molecular cytogenetics and genomics [7,8,9]. Furthermore, somatic changes of the cellular genome occur throughout the ontogeny, being implicated in development and aging [10,11]. Apparently, the human central nervous system is also affected by the ontogenetic genomic/chromosomal variations despite the post-mitotic nature of brain cells [3,10]. Regardless of the achievements in molecular neurocytogenetics, the questions surrounding the way to uncover chromosomal abnormalities in single cells remain unanswered [3,12,13]. More precisely, whole genome scan data (including data of single-cell analyses) are matched against those obtained by molecular cytogenetic visualization techniques (i.e. fluorescence in situ hybridization or FISH) [3,12,13,14]. In light of this problem, we have suggested that a review dedicated to molecular cytogenetic aspects of studying cellular genomes in the human brain is required.
2. Single-Cell Molecular Cytogenomic Analyses of the Human Brain
Somatic mosaicism seems to possess specific effects on brain development and function. A genetically abnormal neuron may form up to ten thousand connections with other neurons altering their functions. This ability of neuronal cells has been suggested to underlie the effect of somatic genome variations (mainly, aneuploidy) on brain function in health and disease [1,5,15]. Since aneuploidy involves from hundreds to thousands of genes, such genomic variations can dramatically alter cellular homeostasis [16,17,18]. Thus, there is a strong theoretic background for uncovering genomic variations in single brain cells or, in other words, molecular neurocytogenetic research.
FISH-based molecular cytogenetic studies revealed the developing brain to be the fetal tissue featured by confined chromosomal mosaicism. The developing human brain was additionally featured by chromosome instability or sporadic aneuploidy affecting more than 30% of cells [19,20,21]. In the adult brain, aneuploidy seemed to affect up to 10% of cells [22,23,24,25,26]. Interestingly, the rate of decrease in cellular populations of the brain during the early ontogeny correlated with the rate of decrease in the amount of aneuploid cells in the developing human central nervous system, allowing for the speculations about the role of chromosomal instability/aneuploidy in regulating cellular populations in the human brain [6,10,17,20,27]. These ontogenetic genomic variations were later considered an empirical basis for designing forthcoming studies of somatic mosaicism in the human brain [27,28,29]. However, since single-cell genome scanning techniques demonstrated <3% of aneuploidy cells in the normal adult human brain [14,21,26,30], addressing technological aspects of molecular neurocytogenetics were required.
Somatic mosaicism is recognized as a genetic mechanism for a variety of brain diseases [7,15,31,32]. More importantly, somatic mosaicism and genome (chromosome) instability mediates neurodegeneration [25,33,34,35], suggesting that these phenomena are a molecular link between neurodegeneration, cancer and aging [25,36]. In this context, it is to mention that the contribution of somatic mosaicism and chromosome instability to brain aging remains a matter of conjecture, mainly due to data differences between single-cell whole genome analysis and molecular cytogenetic studies [37,38,39,40].
At the beginning of molecular neurocytogenetics, observations demonstrating functional activity and integration of genomically abnormal neurons into brain circuitry suggested brain specific chromosomal mosaicism/instability as a mechanism for psychiatric and neurological disorders (for more details, see ). Thus, schizophrenia and comorbid psychiatric disorders were associated with mosaic aneuploidy [42,43,44], chromosome-1-specific instability  and brain-specific copy number variations  in a significant proportion of cases. Appreciable number of autism cases was suggested to be also associated with mosaic aneuploidy [46,47]. These findings were recently used for proposing a cytogenomic hypothesis, which suggested behavioral variability to be mediated by fluctuations of somatic mosaicism rates . Probably, interphase chromosome breaks, which are exclusively uncovered by FISH-based molecular neurocytogenetic approaches, might contribute to the behavioral changes highlighted in the cytogenomic hypothesis.
Alzheimer’s disease was also repeatedly associated with high rates of mosaic aneuploidy (for more details, see [23,24,33,49]). For instance, the Alzheimer’s disease brain exhibited high rates of X chromosome aneuploidy, which suggest a link between brain aging (aging is commonly associated with X chromosome aneuploidy) and this devastative neurodegenerative disorder . The disorder was also associated with regional mosaic genomic heterogeneity  and chromosome/genome instability [24,52,53]. However, single-cell genomic analyses questioned the amount of aneuploidy cells in the Alzheimer’s disease brain . Additionally, aneuploidization was shown to be likely involved in the pathogenesis of Lewy body diseases  and frontotemporal lobar degeneration . Finally, the aforementioned neurocytogenetic discoveries appeared to be important for developing therapeutic strategies for brain dysfunction , molecular diagnostic approaches to brain pathology [28,57,58], and unraveling molecular and cellular pathways for mental illness [3,17,52,58,59]. In total, single-cell molecular cytogenomic analyses of the human brain are an important part of current biomedicine. The following part is dedicated to advantages/disadvantages of FISH-based approaches and their possible applications in the post-genomic era.
3. Molecular Neurocytogenetics: Technological Issues
Technological aspects are hard to overestimate in reviewing neurocytogenetic studies. The development of FISH-based approaches to the visualization of specific chromosomal regions or whole interphase chromosomes in their integrity during all stages of the cell cycle has initially launched the molecular neurocytogenetic analysis of the human central nervous system (for more details, see [20,22,24,25]). Alternative platform for studying brain specific intercellular genomic variations in the brain is based on newly developed techniques of single-cell whole-genome analysis by next-generation sequencing (for more details, see [14,21,30]). Since the latter platform is generally recognized to be superior to single-cell molecular cytogenetic (microscopic) analysis, there is a need for consistent re-evaluation of the role FISH-based approaches play in single cell genomics.
Although molecular genetic techniques are able to uncover mosaic chromosomal abnormalities and copy number variations in whole fractions of DNA isolated from large cellular populations [4,13,27,60], molecular (interphase) cytogenetic techniques offer numerous additional opportunities for singe-cell chromosomal analysis (for more details, see [12,61]). It is important to mention that a number of molecular cytogenetic FISH-based methods have been the results of technological developments in molecular neurocytogenetics [3,61].
Initially, molecular cytogenetic studies were technologically based on multiprobe FISH with chromosome-enumeration DNA probes [1,15,19]. Unfortunately, human brain cells exhibit specific nuclear organization referred to high rates of interphase chromosome associations [1,22], which are not discriminated by multiprobe FISH. To solve this problem, quantitative FISH (digital quantification of FISH results allowing the differentiation between chromosome loss and chromosomal associations) may be applied . Multiprobe FISH with quantitative FISH are unable to depict the whole interphase chromosome in its integrity [12,61]. To see an interphase chromosome in its integrity and in any cell type with molecular resolutions, interphase chromosome-specific multicolor banding has been developed [22,63]. This is the only visualization (microscopic) technique offering the possibility to detect interphase chromosome instability without unbalanced genome changes (i.e. chromosomal breaks, fragility etc.) [25,61]. Despite possibilities for detecting recurrent DNA break clusters , a comprehensive alternative to interphase chromosome-specific multicolour banding for uncovering all types of chromosomal instability does not exist.
As it was already mentioned, data on brain-specific aneuploidy obtained by molecular (interphase) cytogenetic techniques are appreciably different from those obtained by single-cell molecular genetic methods. Interphase FISH approaches to molecular cytogenetic analyses of the human brain have high cell scoring potential (more than 5,000-10,000 interphase nuclei per sample/probe set) [1,4,8,61]. However, these techniques are unable to uncover whole spectrum of genomic variations detectable by single-cell next-generation sequencing. The latter, on the other hand, has very low cell scoring potential (i.e. less than 100 cells per sample) [21,30]. High cell scoring significantly contributes to the success of molecular neurocytogenetic studies, because aneuploid brain cells are extremely rare in cases of chromosome-specific aneuploidy [19,20,21,22,23,24]. In addition, it is to note that interphase FISH is not free from false-positive/false-negative artifacts produced by unusual signal appearance due to specific nuclear chromosome organization [1,3,22]. As mentioned before, to solve this problem, interphase cytogenetic approaches to chromosome instability in the human brain appear to require the application of quantitative FISH and interphase chromosome-specific multicolor banding.
There are several disadvantages leading to false-positive/false-negative results of both molecular (interphase) cytogenetic (for review, see [1,2,3,4,5]) and single-cell whole-genome scanning techniques (i.e. impossibility to correlate the results of whole genome amplification and the true genome variations in a cell; moderate/low cell scoring potential; for more details, see [3,4,5,13]). Accordingly, the rationale for successful analysis of genomic variations in the human brain is to combine data acquired by FISH-based techniques with whole-genome data on individual cells or whole fraction of DNA isolated from large cellular populations. Table 1 summarizes the molecular neurocytogenetic findings and technological issues.
Table 1 Molecular neurocytogenetic findings in the technological context.
4. Conclusions and Future Prospects
The applications of FISH-based methods to molecular neurocytogenetic purposes are not limited to detecting chromosomal abnormalities, inasmuch as these may uncover chromosome instability and behavior that are undetectable by single-cell molecular genetic techniques. Even in the post-genomic era, FISH remains an important methodology for human genome analysis . Despite a number of unresolved disadvantages, the resolution of single-cell whole-genome analysis by next-generation sequencing defines this platform as an efficient tool for studying neuronal genomes. In light of this, one can suggest that there is a need for developing a technique that combines microscopic FISH-based methods with single-cell whole-genome scan. Still, multilateral analysis of neuronal genome behavior at the chromosomal level cannot be appropriately performed without the application of FISH-based technologies. To this end, we suggest that further studies dedicated to uncovering instable cellular genomes in the brain should be targeted on identification of the causes and consequences using post-genomic (empirical and bioinformatic) techniques and molecular cytogenetic methods. These data is applicable for understanding mechanisms and discovering new therapeutic targets in brain diseases.
The review is dedicated to Ilia Soloviev.
YBY, SGV, and IYI conceived the underlying ideas and basics of molecular neurocytogenetics. IYI wrote the manuscript.
This work was supported by RFBR and CITMA according to the research project №18-515-34005.
The authors have declared that no competing interests exist.
- Iourov IY, Vorsanova SG, Yurov YB. Chromosomal variation in mammalian neuronal cells: known facts and attractive hypotheses. Int Rev Cytol. 2006; 249: 143-191. [CrossRef]
- Kingsbury MA, Yung YC, Peterson SE, Westra JW, Chun J. Aneuploidy in the normal and diseased brain. Cell Mol Life Sci. 2006; 63: 2626-2641. [CrossRef]
- Yurov YB, Vorsanova SG, Iourov IY. Human molecular neurocytogenetics. Curr Genet Med Rep. 2018; 6: 155-164. [CrossRef]
- Iourov IY, Vorsanova SG, Yurov YB. Single cell genomics of the brain: focus on neuronal diversity and neuropsychiatric diseases. Curr Genomics. 2012; 13: 477-488. [CrossRef]
- Rohrback S, Siddoway B, Liu CS, Chun J. Genomic mosaicism in the developing and adult brain. Dev Neurobiol. 2018; 78: 1026-1048. [CrossRef]
- Muotri AR, Gage FH. Generation of neuronal variability and complexity. Nature. 2006; 441: 1087-1093. [CrossRef]
- Iourov IY, Vorsanova SG, Yurov YB. Somatic genome variations in health and disease. Curr Genomics. 2010; 11: 387-396. [CrossRef]
- Bushman DM, Chun J. The genomically mosaic brain: aneuploidy and more in neural diversity and disease. Semin Cell Dev Biol. 2013; 24: 357-369. [CrossRef]
- Campbell IM, Shaw CA, Stankiewicz P, Lupski JR. Somatic mosaicism: implications for disease and transmission genetics. Trends Genet. 2015; 31: 382-392. [CrossRef]
- Yurov YB, Vorsanova SG, Iourov IY. Ontogenetic variation of the human genome. Curr Genomics. 2010; 11: 420-425. [CrossRef]
- Taylor TH, Gitlin SA, Patrick JL, Crain JL, Wilson JM, Griffin DK. The origin, mechanisms, incidence and clinical consequences of chromosomal mosaicism in humans. Hum Reprod Update. 2014; 20: 571-581. [CrossRef]
- Yurov YB, Vorsanova SG, Iourov IY. Human interphase chromosomes: biomedical aspects. New York: Springer-Verlag; 2013. [CrossRef]
- Bakker B, van den Bos H, Lansdorp PM, Foijer F. How to count chromosomes in a cell: an overview of current and novel technologies. BioEssays. 2015; 37: 570-577. [CrossRef]
- van den Bos H, Spierings DC, Taudt AS, Bakker B, Porubský D, Falconer E, et al. Single-cell whole genome sequencing reveals no evidence for common aneuploidy in normal and Alzheimer’s disease neurons. Genome Biol. 2016; 17: 116. [CrossRef]
- Iourov IY, Vorsanova SG, Yurov YB. Molecular cytogenetics and cytogenomics of brain diseases. Curr Genomics. 2008; 9: 452-465. [CrossRef]
- Siegel JJ, Amon A. New insights into the troubles of aneuploidy. Annu Rev Cell Dev Biol. 2012; 28: 189-214. [CrossRef]
- Paquola ACM, Erwin JA, Gage FH. Insights into the role of somatic mosaicism in the brain. Curr Opin Syst Biol. 2017; 1: 90-94. [CrossRef]
- Chunduri NK, Storchová Z. The diverse consequences of aneuploidy. Nat Cell Biol. 2019; 21: 54-62. [CrossRef]
- Yurov YB, Iourov IY, Monakhov VV, Soloviev IV, Vostrikov VM, Vorsanova SG. The variation of aneuploidy frequency in the developing and adult human brain revealed by an interphase FISH study. J Histochem Cytochem. 2005; 53: 385-390. [CrossRef]
- Yurov YB, Iourov IY, Vorsanova SG, Liehr T, Kolotii AD, Kutsev SI, et al. Aneuploidy and confined chromosomal mosaicism in the developing human brain. PLoS One. 2007; 2: e558. [CrossRef]
- Rohrback S, April C, Kaper F, Rivera RR, Liu CS, Siddoway B, Chun J. Submegabase copy number variations arise during cerebral cortical neurogenesis as revealed by single-cell whole-genome sequencing. Proc Natl Acad Sci USA. 2018; 115: 10804-10809. [CrossRef]
- Iourov IY, Liehr T, Vorsanova SG, Kolotii AD, Yurov YB. Visualization of interphase chromosomes in postmitotic cells of the human brain by multicolour banding (MCB). Chromosom Res. 2006; 14: 223-229. [CrossRef]
- Mosch B, Morawski M, Mittag A, Lenz D, Tarnok A, Arendt T. Aneuploidy and DNA replication in the normal human brain and Alzheimer's disease. J Neurosci. 2007; 27: 6859-6867. [CrossRef]
- Iourov IY, Vorsanova SG, Liehr T, Yurov YB. Aneuploidy in the normal, Alzheimer's disease and ataxia-telangiectasia brain: differential expression and pathological meaning. Neurobiol Dis. 2009; 34: 212-220. [CrossRef]
- Iourov IY, Vorsanova SG, Liehr T, Kolotii AD, Yurov YB. Increased chromosome instability dramatically disrupts neural genome integrity and mediates cerebellar degeneration in the ataxia-telangiectasia brain. Hum Mol Genet. 2009; 18: 2656-2669. [CrossRef]
- McConnell MJ, Lindberg MR, Brennand KJ, Piper JC, Voet T, Cowing-Zitron C, et al. Mosaic copy number variation in human neurons. Science. 2013; 342: 632-637. [CrossRef]
- Arendt T, Mosch B, Morawski M. Neuronal aneuploidy in health and disease: A cytomic approach to understand the molecular individuality of neurons. Int J Mol Sci. 2009; 10: 1609-1627. [CrossRef]
- Iourov IY, Vorsanova SG, Liehr T, Yurov YB. Mosaike im gehirn des menschen. Med Genet. 2014; 26: 342-345. [CrossRef]
- McConnell MJ, Moran JV, Abyzov A, Akbarian S, Bae T, Brain Somatic Mosaicism Network, et al. Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network. Science. 2017; 356. pii: eaal1641. [CrossRef]
- Knouse KA, Wu J, Whittaker CA, Amon A. Single cell sequencing reveals low levels of aneuploidy across mammalian tissues. Proc Natl Acad Sci USA. 2014; 111: 13409-13414. [CrossRef]
- Campbell IM, Yuan B, Robberecht C, Pfundt R, Szafranski P, McEntagart ME, et al. Parental somatic mosaicism is underrecognized and influences recurrence risk of genomic disorders. Am J Hum Genet. 2014; 95: 173-182. [CrossRef]
- D'Gama AM, Walsh CA. Somatic mosaicism and neurodevelopmental disease. Nat Neurosci. 2018; 21: 1504-1514. [CrossRef]
- Iourov IY, Vorsanova SG, Yurov YB. Genomic landscape of the Alzheimer’s disease brain: chromosome instability—aneuploidy, but not tetraploidy—mediates neurodegeneration. Neurodegener Dis. 2011, 8: 35-37. [CrossRef]
- Leija-Salazar M, Piette C, Proukakis C. Review: somatic mutations in neurodegeneration. Neuropathol Appl Neurobiol. 2018; 44: 267-285. [CrossRef]
- Shepherd CE, Yang Y, Halliday GM. Region- and cell-specific aneuploidy in brain aging and neurodegeneration. Neuroscience. 2018; 374: 326-334. [CrossRef]
- Kennedy SR, Loeb LA, Herr AJ. Somatic mutations in aging, cancer and neurodegeneration. Mech Ageing Dev. 2012; 133: 118-126. [CrossRef]
- Fischer HG, Morawski M, Brückner MK, Mittag A, Tarnok A, Arendt T. Changes in neuronal DNA content variation in the human brain during aging. Aging Cell. 2012; 11: 628-633. [CrossRef]
- Zhang L, Vijg J. Somatic mutagenesis in mammals and its implications for human disease and aging. Annu Rev Genet. 2018; 52: 397-419. [CrossRef]
- Andriani GA, Vijg J, Montagna C. Mechanisms and consequences of aneuploidy and chromosome instability in the aging brain. Mech Ageing Dev. 2017; 161: 19-36. [CrossRef]
- Chronister WD, Burbulis IE, Wierman MB, Wolpert MJ, Haakenson MF, Smith ACB, et al. Neurons with complex karyotypes are rare in aged human neocortex. Cell Rep. 2019; 26: 825-835. [CrossRef]
- Kingsbury MA, Friedman B, McConnell MJ, Rehen SK, Yang AH, Kaushal D, et al. Aneuploid neurons are functionally active and integrated into brain circuitry. Proc Natl Acad Sci USA. 2005; 102: 6143-6147. [CrossRef]
- Yurov YB, Vostrikov VM, Vorsanova SG, Monakhov VV, Iourov IY. Multicolor fluorescent in situ hybridization on post-mortem brain in schizophrenia as an approach for identification of low-level chromosomal aneuploidy in neuropsychiatric diseases. Brain Dev. 2001; 23: S186-S190. [CrossRef]
- Yurov YB, Iourov IY, Vorsanova SG, Demidova IA, Kravetz VS, Beresheva AK, et al. The schizophrenia brain exhibits low-level aneuploidy involving chromosome 1. Schizophr Res. 2008; 98: 139-147. [CrossRef]
- Yurov YB, Vorsanova SG, Demidova IA, Kolotii AD, Soloviev IV, Iourov IY. Mosaic brain aneuploidy in mental illnesses: an association of low-level post-zygotic aneuploidy with schizophrenia and comorbid psychiatric disorders. Curr Genomics. 2018; 19: 163-172. [CrossRef]
- Sakai M, Watanabe Y, Someya T, Araki K, Shibuya M, Niizato K, et al. Assessment of copy number variations in the brain genome of schizophrenia patients. Mol Cytogenet. 2015; 8: 46. [CrossRef]
- Yurov YB, Vorsanova SG, Iourov IY, Demidova IA, Beresheva AK, Kravetz VS, et al. Unexplained autism is frequently associated with low-level mosaic aneuploidy. J Med Genet. 2007; 44: 521-525. [CrossRef]
- Vorsanova SG, Voinova VY, Yurov IY, Kurinnaya OS, Demidova IA, Yurov YB. Cytogenetic, molecular-cytogenetic, and clinical-genealogical studies of the mothers of children with autism: a search for familial genetic markers for autistic disorders. Neurosci Behav Physiol. 2010; 40: 745-756. [CrossRef]
- Vorsanova SG, Zelenova MA, Yurov YB, Iourov IY. Behavioral variability and somatic mosaicism: a cytogenomic hypothesis. Curr Genomics. 2018; 19: 158-162. [CrossRef]
- Bajic V, Spremo-Potparevic B, Zivkovic L, Isenovic ER, Arendt T. Cohesion and the aneuploid phenotype in Alzheimer’s disease: A tale of genome instability. Neurosci Biobehav Rev. 2015; 55: 365-374. [CrossRef]
- Yurov YB, Vorsanova SG, Liehr T, Kolotii AD, Iourov IY. X chromosome aneuploidy in the Alzheimer’s disease brain. Mol Cytogenet. 2014; 7: 20. [CrossRef]
- Arendt T, Brückner MK, Lösche A. Regional mosaic genomic heterogeneity in the elderly and in Alzheimer's disease as a correlate of neuronal vulnerability. Acta Neuropathol. 2015; 130: 501-510. [CrossRef]
- Yurov YB, Vorsanova SG, Iourov IY. The DNA replication stress hypothesis of Alzheimer’s disease. ScientificWorldJournal. 2011; 11: 2602-2612. [CrossRef]
- Lee MH, Siddoway B, Kaeser GE, Segota I, Rivera R, Romanow WJ, Liu CS, Park C, Kennedy G, Long T, Chun J. Somatic APP gene recombination in Alzheimer's disease and normal neurons. Nature. 2018; 563: 639-645. [CrossRef]
- Yang Y, Shepherd C, Halliday G. Aneuploidy in Lewy body diseases. Neurobiol Aging. 2015; 36: 1253-1260. [CrossRef]
- Caneus J, Granic A, Rademakers R, Dickson DW, Coughlan CM, Chial HJ, et al. Mitotic defects lead to neuronal aneuploidy and apoptosis in frontotemporal lobar degeneration caused by MAPT mutations. Mol Biol Cell. 2018; 29: 575-586. [CrossRef]
- Devalle S, Sartore RC, Paulsen BS, Borges HL, Martins RA, Rehen SK. Implications of aneuploidy for stem cell biology and brain therapeutics. Front Cell Neurosci. 2012; 6: 36. [CrossRef]
- Vorsanova SG, Yurov YB, Soloviev IV, Iourov IY. Molecular cytogenetic diagnosis and somatic genome variations. Curr Genomics. 2010; 11: 440-446. [CrossRef]
- Lupski JR. Genetics. Genome mosaicism — one human, multiple genomes. Science. 2013; 341: 358-359. [CrossRef]
- Vorsanova SG, Yurov YB, Iourov IY. Neurogenomic pathway of autism spectrum disorders: linking germline and somatic mutations to genetic-environmental interactions. Curr Bioinformatics. 2017; 12: 19-26. [CrossRef]
- Žilina O, Koltšina M, Raid R, Kurg A, Tõnisson N, Salumets A. Somatic mosaicism for copy-neutral loss of heterozygosity and DNA copy number variations in the human genome. BMC Genomics. 2015; 16: 703. [CrossRef]
- Vorsanova SG, Yurov YB, Iourov IY. Human interphase chromosomes: a review of available molecular cytogenetic technologies. Mol Cytogenet. 2010; 3: 1. [CrossRef]
- Iourov IY. Quantitative fluorescence in situ hybridization (QFISH). Methods Mol Biol. 2017; 1541: 143-149. [CrossRef]
- Iourov IY, Liehr T, Vorsanova SG, Yurov YB. Interphase chromosome-specific multicolor banding (ICS-MCB): a new tool for analysis of interphase chromosomes in their integrity. Biomol Eng. 2007; 24: 415-417. [CrossRef]
- Wei PC, Lee CS, Du Z, Schwer B, Zhang Y, Kao J, Zurita J, Alt FW. Three classes of recurrent DNA break clusters in brain progenitors identified by 3D proximity-based break joining assay. Proc Natl Acad Sci USA. 2018; 115: 1919-1924. [CrossRef]
- Weise A, Mrasek K, Pentzold C, Liehr T. Chromosomes in the DNA era: Perspectives in diagnostics and research. Med Genet. 2019; 31: 8-19. [CrossRef]