Single cell heterogeneity: why unstable genomes are incompatible with average profiles

Deciphering cellular heterogeneity in Spodoptera frugiperda midgut cell line through single cell RNA sequencing

Using the 10x Genomics Chromium single-cell RNA sequencing (scRNA-seq) platform, we discovered unexpected heterogeneity in an established cell line developed from the midgut of the Fall armyworm, Spodoptera frugiperda, a major global pest. We analyzed the sequences of 18,794 cells and identified ten unique cellular clusters, including stem cells, enteroblasts, enterocytes and enteroendocrine cells, characterized by the expression of specific marker genes. Additionally, these studies addressed an important knowledge gap by investigating the expression of genes coding for respiratory and midgut membrane insecticide targets classified by the Insecticide Resistance Action Committee. Dual-fluorescence tagging method, fluorescence microscopy and fluorescence-activated cell sorting confirmed the expression of midgut cell type-specific genes. Stem cells were isolated from the heterogeneous population of SfMG-0617 cells. Our results, validated by KEGG and Gene Ontology analyses and supported by Monocle 3.0, advance the fields of midgut cellular biology and establish standards for scRNA-seq studies in non-model organisms.

The New Era of Cancer Cytogenetics and Cytogenomics

The promises of the cancer genome sequencing project, combined with various -omics technologies, have raised questions about the importance of cancer cytogenetic analyses. It is suggested that DNA sequencing provides high resolution, speed, and automation, potentially replacing cytogenetic testing. We disagree with this reductionist prediction. On the contrary, various sequencing projects have unexpectedly challenged gene theory and highlighted the importance of the genome or karyotype in organizing gene network interactions. Consequently, profiling the karyotype can be more meaningful than solely profiling gene mutations, especially in cancer where karyotype alterations mediate cellular macroevolution dominance. In this chapter, recent studies that illustrate the ultimate importance of karyotype in cancer genomics and evolution are briefly reviewed. In particular, the long-ignored non-clonal chromosome aberrations or NCCAs are linked to genome or chromosome instability, genome chaos is linked to genome reorganization under cellular crisis, and the two-phased cancer evolution reconciles the relationship between genome alteration-mediated punctuated macroevolution and gene mutation-mediated stepwise microevolution. By further synthesizing, the concept of karyotype coding is discussed in the context of information management. Altogether, we call for a new era of cancer cytogenetics and cytogenomics, where an array of technical frontiers can be explored further, which is crucial for both basic research and clinical implications in the cancer field.

Optical Genome Mapping: A Machine-Based Platform in Cytogenomics

Optical genome mapping (OGM) has generated excitement following decades of research and development. Now, commercially available technical platforms have been used to compare various other cytogenetic and cytogenomic technologies, including karyotype, microarrays, and DNA sequencing, with impressive results. In this chapter, using OGM as a case study, we advocate for a new trend in future cytogenomics, emphasizing the power of machine automation to deliver higher-quality cytogenomic data. By briefly discussing OGM, along with its major advantages and limitations, we underscore the importance of karyotype-based genomic research, from both a theoretical framework and a new technology perspective. We also call for the encouragement of further technological platform development for the future of cytogenetics and cytogenomics.

Analysis of the Single-Cell Heterogeneity of Adenocarcinoma Cell Lines and the Investigation of Intratumor Heterogeneity Reveals the Expression of Transmembrane Protein 45A (TMEM45A) in Lung Adenocarcinoma Cancer Patients

Simple Summary

Non-small cell lung cancer (NSCLC) is one of the main causes of cancer-related deaths worldwide. Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). Human NSCLC adenocarcinoma cells A549, H1975, and H1650 were studied at single-cell resolution for the expression pattern of 13 markers: GLUT1, MCT4, CA9, TMEM45A, CD66, CD274, CD24, CD326, pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The intra- and inter-cell-line heterogeneity of A549, H1975, and H1650 cells were demonstrated through hypoxic modeling. Additionally, human primary lung adenocarcinoma, and non-involved healthy lung tissue were homogenized to prepare a single-cell suspension for CyTOF analysis. The single-cell heterogeneity was confirmed using unsupervised viSNE and FlowSOM analysis. Our results also show, for the first time, that TMEM45A is expressed in lung adenocarcinoma.

Abstract

Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). First, we studied the single-cell heterogeneity of frequent NSCLC adenocarcinoma models, such as A549, H1975, and H1650. The intra- and inter-cell-line single-cell heterogeneity is represented in the expression patterns of 13 markers—namely GLUT1, MCT4, CA9, TMEM45A, CD66, CD274 (PD-L1), CD24, CD326 (EpCAM), pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The qRT-PCR and CyTOF analyses revealed that a hypoxic microenvironment and altered metabolism may influence cell-line heterogeneity. Additionally, human primary lung adenocarcinoma and non-involved healthy lung tissue biopsies were homogenized to prepare a single-cell suspension for CyTOF analysis. The CyTOF showed the ITH of human primary lung adenocarcinoma for 14 markers; particularly, the higher expressions of GLUT1, MCT4, CA9, TMEM45A, and CD66 were associated with the lung-tumor tissue. Our single-cell results are the first to demonstrate TMEM45A expression in human lung adenocarcinoma, which was verified by immunohistochemistry.

scLINE: A multi-network integration framework based on network embedding for representation of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is fast becoming a powerful technology that revolutionizes biomedical studies related to development, immunology and cancer by providing genome-scale transcriptional profiles at unprecedented throughput and resolution. However, due to the low capture rate and frequent drop-out events in the sequencing process, scRNA-seq data suffer from extremely high sparsity and variability, challenging the data analysis. Here we proposed a novel method called scLINE for learning low dimensional representations of scRNA-seq data. scLINE is based on the network embedding model that jointly considers multiple gene-gene interaction networks, facilitating the incorporation of prior biological knowledge for signal extraction. We comprehensively evaluated scLINE on eight single-cell datasets. Results show that scLINE achieved comparable or higher performance than competing methods, including PCA, t-SNE and Isomap, in terms of internal validation metrics and clustering accuracy. The low dimensional representations learned by scLINE are effective for downstream single-cell analysis, such as visualization, clustering and cell typing. We have implemented scLINE as an easy-to-use R package, which can be incorporated in other existing scRNA-seq analysis pipelines or tools for data preprocessing.

Epigenome Chaos: Stochastic and Deterministic DNA Methylation Events Drive Cancer Evolution

Simple Summary

Cancer is a group of diseases characterized by abnormal cell growth with a high potential to invade other tissues. Genetic abnormalities and epigenetic alterations found in tumors can be due to high levels of DNA damage and repair. These can be transmitted to daughter cells, which assuming other alterations as well, will generate heterogeneous and complex populations. Deciphering this complexity represents a central point for understanding the molecular mechanisms of cancer and its therapy. Here, we summarize the genomic and epigenomic events that occur in cancer and discuss novel approaches to analyze the epigenetic complexity of cancer cell populations.

Abstract

Cancer evolution is associated with genomic instability and epigenetic alterations, which contribute to the inter and intra tumor heterogeneity, making genetic markers not accurate to monitor tumor evolution. Epigenetic changes, aberrant DNA methylation and modifications of chromatin proteins, determine the “epigenome chaos”, which means that the changes of epigenetic traits are randomly generated, but strongly selected by deterministic events. Disordered changes of DNA methylation profiles are the hallmarks of all cancer types, but it is not clear if aberrant methylation is the cause or the consequence of cancer evolution. Critical points to address are the profound epigenetic intra- and inter-tumor heterogeneity and the nature of the heterogeneity of the methylation patterns in each single cell in the tumor population. To analyze the methylation heterogeneity of tumors, new technological and informatic tools have been developed. This review discusses the state of the art of DNA methylation analysis and new approaches to reduce or solve the complexity of methylated alleles in DNA or cell populations.

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

Genome chaos: Creating new genomic information essential for cancer macroevolution

Cancer research has traditionally focused on the characterization of individual molecular mechanisms that can contribute to cancer. Due to the multiple levels of genomic and non-genomic heterogeneity, however, overwhelming molecular mechanisms have been identified, most with low clinical predictability. It is thus necessary to search for new concepts to unify these diverse mechanisms and develop better strategies to understand and treat cancer. In recent years, two-phased cancer evolution (comprised of the genome reorganization-mediated punctuated phase and gene mutation-mediated stepwise phase), initially described by tracing karyotype evolution, was confirmed by the Cancer Genome Project. In particular, genome chaos, the process of rapid and massive genome reorganization, has been commonly detected in various cancers-especially during key phase transitions, including cellular transformation, metastasis, and drug resistance-suggesting the importance of genome-level changes in cancer evolution. In this Perspective, genome chaos is used as a discussion point to illustrate new genome-mediated somatic evolutionary frameworks. By rephrasing cancer as a new system emergent from normal tissue, we present the multiple levels (or scales) of genomic and non-genomic information. Of these levels, evolutionary studies at the chromosomal level are determined to be of ultimate importance, since altered genomes change the karyotype coding and karyotype change is the key event for punctuated cellular macroevolution. Using this lens, we differentiate and analyze developmental processes and cancer evolution, as well as compare the informational relationship between genome chaos and its various subtypes in the context of macroevolution under crisis. Furthermore, the process of deterministic genome chaos is discussed to interpret apparently random events (including stressors, chromosomal variation subtypes, surviving cells with new karyotypes, and emergent stable cellular populations) as nonrandom patterns, which supports the new cancer evolutionary model that unifies genome and gene contributions during different phases of cancer evolution. Finally, the new perspective of using cancer as a model for organismal evolution is briefly addressed, emphasizing the Genome Theory as a new and necessary conceptual framework for future research and its practical implications, not only in cancer but evolutionary biology as a whole.

Origins and Consequences of Chromosomal Instability: From Cellular Adaptation to Genome Chaos-Mediated System Survival

When discussing chromosomal instability, most of the literature focuses on the characterization of individual molecular mechanisms. These studies search for genomic and environmental causes and consequences of chromosomal instability in cancer, aiming to identify key triggering factors useful to control chromosomal instability and apply this knowledge in the clinic. Since cancer is a phenomenon of new system emergence from normal tissue driven by somatic evolution, such studies should be done in the context of new genome system emergence during evolution. In this perspective, both the origin and key outcome of chromosomal instability are examined using the genome theory of cancer evolution. Specifically, chromosomal instability was linked to a spectrum of genomic and non-genomic variants, from epigenetic alterations to drastic genome chaos. These highly diverse factors were then unified by the evolutionary mechanism of cancer. Following identification of the hidden link between cellular adaptation (positive and essential) and its trade-off (unavoidable and negative) of chromosomal instability, why chromosomal instability is the main player in the macro-cellular evolution of cancer is briefly discussed. Finally, new research directions are suggested, including searching for a common mechanism of evolutionary phase transition, establishing chromosomal instability as an evolutionary biomarker, validating the new two-phase evolutionary model of cancer, and applying such a model to improve clinical outcomes and to understand the genome-defined mechanism of organismal evolution.

Genomic Indexing by Somatic Gene Recombination of mRNA/ncRNA - Does It Play a Role in Genomic Mosaicism, Memory Formation, and Alzheimer's Disease?

Recent evidence indicates that genomic individuality of neurons, characterized by DNA-content variation, is a common if not universal phenomenon in the human brain that occurs naturally but can also show aberrancies that have been linked to the pathomechanism of Alzheimer’s disease and related neurodegenerative disorders. Etiologically, this genomic mosaic has been suggested to arise from defects of cell cycle regulation that may occur either during brain development or in the mature brain after terminal differentiation of neurons. Here, we aim to draw attention towards another mechanism that can give rise to genomic individuality of neurons, with far-reaching consequences. This mechanism has its origin in the transcriptome rather than in replication defects of the genome, i.e., somatic gene recombination of RNA. We continue to develop the concept that somatic gene recombination of RNA provides a physiological process that, through integration of intronless mRNA/ncRNA into the genome, allows a particular functional state at the level of the individual neuron to be indexed. By insertion of defined RNAs in a somatic recombination process, the presence of specific mRNA transcripts within a definite temporal context can be “frozen” and can serve as an index that can be recalled at any later point in time. This allows information related to a specific neuronal state of differentiation and/or activity relevant to a memory trace to be fixed. We suggest that this process is used throughout the lifetime of each neuron and might have both advantageous and deleterious consequences.

Single-cell analysis for drug development using convex lens-induced confinement imaging

New technologies have powered rapid advances in cellular imaging, genomics and phenotypic analysis in life sciences. However, most of these methods operate at sample population levels and provide statistical averages of aggregated data that fail to capture single-cell heterogeneity, complicating drug discovery and development. Here we demonstrate a new single-cell approach based on convex lens-induced confinement (CLiC) microscopy. We validated CLiC on yeast cells, demonstrating subcellular localization with an enhanced signal-to-noise and fluorescent signal detection sensitivity compared with traditional imaging. In the live-cell CLiC assay, cellular proliferation times were consistent with flask culture. Using methotrexate, we provide drug response data showing a fivefold cell size increase following drug exposure. Taken together, CLiC enables high-quality imaging of single-cell drug response and proliferation for extended observation periods.

Ontogenetic and Pathogenetic Views on Somatic Chromosomal Mosaicism

Intercellular karyotypic variability has been a focus of genetic research for more than 50 years. It has been repeatedly shown that chromosome heterogeneity manifesting as chromosomal mosaicism is associated with a variety of human diseases. Due to the ability of changing dynamically throughout the ontogeny, chromosomal mosaicism may mediate genome/chromosome instability and intercellular diversity in health and disease in a bottleneck fashion. However, the ubiquity of negligibly small populations of cells with abnormal karyotypes results in difficulties of the interpretation and detection, which may be nonetheless solved by post-genomic cytogenomic technologies. In the post-genomic era, it has become possible to uncover molecular and cellular pathways to genome/chromosome instability (chromosomal mosaicism or heterogeneity) using advanced whole-genome scanning technologies and bioinformatic tools. Furthermore, the opportunities to determine the effect of chromosomal abnormalities on the cellular phenotype seem to be useful for uncovering the intrinsic consequences of chromosomal mosaicism. Accordingly, a post-genomic review of chromosomal mosaicism in the ontogenetic and pathogenetic contexts appears to be required. Here, we review chromosomal mosaicism in its widest sense and discuss further directions of cyto(post)genomic research dedicated to chromosomal heterogeneity.

Micronuclei and Genome Chaos: Changing the System Inheritance

Micronuclei research has regained its popularity due to the realization that genome chaos, a rapid and massive genome re-organization under stress, represents a major common mechanism for punctuated cancer evolution. The molecular link between micronuclei and chromothripsis (one subtype of genome chaos which has a selection advantage due to the limited local scales of chromosome re-organization), has recently become a hot topic, especially since the link between micronuclei and immune activation has been identified. Many diverse molecular mechanisms have been illustrated to explain the causative relationship between micronuclei and genome chaos. However, the newly revealed complexity also causes confusion regarding the common mechanisms of micronuclei and their impact on genomic systems. To make sense of these diverse and even conflicting observations, the genome theory is applied in order to explain a stress mediated common mechanism of the generation of micronuclei and their contribution to somatic evolution by altering the original set of information and system inheritance in which cellular selection functions. To achieve this goal, a history and a current new trend of micronuclei research is briefly reviewed, followed by a review of arising key issues essential in advancing the field, including the re-classification of micronuclei and how to unify diverse molecular characterizations. The mechanistic understanding of micronuclei and their biological function is re-examined based on the genome theory. Specifically, such analyses propose that micronuclei represent an effective way in changing the system inheritance by altering the coding of chromosomes, which belongs to the common evolutionary mechanism of cellular adaptation and its trade-off. Further studies of the role of micronuclei in disease need to be focused on the behavior of the adaptive system rather than specific molecular mechanisms that generate micronuclei. This new model can clarify issues important to stress induced micronuclei and genome instability, the formation and maintenance of genomic information, and cellular evolution essential in many common and complex diseases such as cancer.

Genomic chaos in peripheral blood lymphocytes of Hodgkin's lymphoma patients one year after ABVD chemotherapy/radiotherapy

Hodgkin's lymphoma (HL) is a lymphoid malignancy representing 5% of all cancers in children, 16% in adolescents, and 30-40% of all malignant lymphomas and has a survival rate of ~95% at 10 years. One of the most common treatment schemes uses a cocktail of genotoxic agents including adriamycin, bleomycin, vinblastine, and dacarbazine (ABVD) with or without radiotherapy. We investigated the occurrence of chromosomal damage in peripheral blood lymphocytes from five patients diagnosed with HL who provided samples before (BT), during chemotherapy (DT) and ~1 year after ABVD chemotherapy/radiotherapy (AT). Five healthy subjects served as controls. Chromosomal abnormalities were evaluated by multicolor fluorescence in situ hybridization. The average frequencies of structural chromosomal aberrations in HL samples were 0.11, 0.22, and 0.96 per cell in BT, DT, and AT samples, respectively. These frequencies were significantly different (P < 0.0001) with respect to control subjects (0.02 per cell). Interestingly, the highest frequency of structural damage, including genomic chaos and nonclonal abnormalities, was observed in the AT samples indicating that new aberrations were continuously produced. Rejoined structural chromosomal aberrations were the most common type of aberrations, although aneuploidies were also significantly increased. Finally, we found several chromosomal abnormalities linked to cancer secondary to treatment in all five HL patients. Our results show that ABVD chemotherapy plus radiotherapy is inducing genomic chaos in vivo; moreover, the persistence of genomic instability in the hematopoietic stem cells from HL patients may play a role in the occurrence of secondary cancer that is observed in 5-20% of HL patients. Environ. Mol. Mutagen. 59:755-768, 2018. © 2018 Wiley Periodicals, Inc.

Understanding aneuploidy in cancer through the lens of system inheritance, fuzzy inheritance and emergence of new genome systems

BACKGROUND

In the past 15 years, impressive progress has been made to understand the molecular mechanism behind aneuploidy, largely due to the effort of using various -omics approaches to study model systems (e.g. yeast and mouse models) and patient samples, as well as the new realization that chromosome alteration-mediated genome instability plays the key role in cancer. As the molecular characterization of the causes and effects of aneuploidy progresses, the search for the general mechanism of how aneuploidy contributes to cancer becomes increasingly challenging: since aneuploidy can be linked to diverse molecular pathways (in regards to both cause and effect), the chances of it being cancerous is highly context-dependent, making it more difficult to study than individual molecular mechanisms. When so many genomic and environmental factors can be linked to aneuploidy, and most of them not commonly shared among patients, the practical value of characterizing additional genetic/epigenetic factors contributing to aneuploidy decreases.

RESULTS

Based on the fact that cancer typically represents a complex adaptive system, where there is no linear relationship between lower-level agents (such as each individual gene mutation) and emergent properties (such as cancer phenotypes), we call for a new strategy based on the evolutionary mechanism of aneuploidy in cancer, rather than continuous analysis of various individual molecular mechanisms. To illustrate our viewpoint, we have briefly reviewed both the progress and challenges in this field, suggesting the incorporation of an evolutionary-based mechanism to unify diverse molecular mechanisms. To further clarify this rationale, we will discuss some key concepts of the genome theory of cancer evolution, including system inheritance, fuzzy inheritance, and cancer as a newly emergent cellular system.

CONCLUSION

Illustrating how aneuploidy impacts system inheritance, fuzzy inheritance and the emergence of new systems is of great importance. Such synthesis encourages efforts to apply the principles/approaches of complex adaptive systems to ultimately understand aneuploidy in cancer.

A Postgenomic Perspective on Molecular Cytogenetics

BACKGROUND

The postgenomic era is featured by massive data collection and analyses from various large scale-omics studies. Despite the promising capability of systems biology and bioinformatics to handle large data sets, data interpretation, especially the translation of -omics data into clinical implications, has been challenging.

DISCUSSION

In this perspective, some important conceptual and technological limitations of current systems biology are discussed in the context of the ultimate importance of the genome beyond the collection of all genes. Following a brief summary of the contributions of molecular cytogenetics/cytogenomics in the pre- and post-genomic eras, new challenges for postgenomic research are discussed. Such discussion leads to a call to search for a new conceptual framework and holistic methodologies.

CONCLUSION

Throughout this synthesis, the genome theory of somatic cell evolution is highlighted in contrast to gene theory, which ignores the karyotype-mediated higher level of genetic information. Since "system inheritance" is defined by the genome context (gene content and genomic topology) while "parts inheritance" is defined by genes/epigenes, molecular cytogenetics and cytogenomics (which directly study genome structure, function, alteration and evolution) will play important roles in this postgenomic era.

Versatile approach for functional analysis of human proteins and efficient stable cell line generation using FLP-mediated recombination system

Deciphering a function of a given protein requires investigating various biological aspects. Usually, the protein of interest is expressed with a fusion tag that aids or allows subsequent analyses. Additionally, downregulation or inactivation of the studied gene enables functional studies. Development of the CRISPR/Cas9 methodology opened many possibilities but in many cases it is restricted to non-essential genes. Recombinase-dependent gene integration methods, like the Flp-In system, are very good alternatives. The system is widely used in different research areas, which calls for the existence of compatible vectors and efficient protocols that ensure straightforward DNA cloning and generation of stable cell lines. We have created and validated a robust series of 52 vectors for streamlined generation of stable mammalian cell lines using the FLP recombinase-based methodology. Using the sequence-independent DNA cloning method all constructs for a given coding-sequence can be made with just three universal PCR primers. Our collection allows tetracycline-inducible expression of proteins with various tags suitable for protein localization, FRET, bimolecular fluorescence complementation (BiFC), protein dynamics studies (FRAP), co-immunoprecipitation, the RNA tethering assay and cell sorting. Some of the vectors contain a bidirectional promoter for concomitant expression of miRNA and mRNA, so that a gene can be silenced and its product replaced by a mutated miRNA-insensitive version. Our toolkit and protocols have allowed us to create more than 500 constructs with ease. We demonstrate the efficacy of our vectors by creating stable cell lines with various tagged proteins (numatrin, fibrillarin, coilin, centrin, THOC5, PCNA). We have analysed transgene expression over time to provide a guideline for future experiments and compared the effectiveness of commonly used inducers for tetracycline-responsive promoters. As proof of concept we examined the role of the exoribonuclease XRN2 in transcription termination by RNAseq.

A short review of variants calling for single-cell-sequencing data with applications

The field of single-cell sequencing is fleetly expanding, and many techniques have been developed in the past decade. With this technology, biologists can study not only the heterogeneity between two adjacent cells in the same tissue or organ, but also the evolutionary relationships and degenerative processes in a single cell. Calling variants is the main purpose in analyzing single cell sequencing (SCS) data. Currently, some popular methods used for bulk-cell-sequencing data analysis are tailored directly to be applied in dealing with SCS data. However, SCS requires an extra step of genome amplification to accumulate enough quantity for satisfying sequencing needs. The amplification yields large biases and thus raises challenge for using the bulk-cell-sequencing methods. In order to provide guidance for the development of specialized analyzed methods as well as using currently developed tools for SNS, this paper aims to bridge the gap. In this paper, we firstly introduced two popular genome amplification methods and compared their capabilities. Then we introduced a few popular models for calling single-nucleotide polymorphisms and copy-number variations. Finally, break-through applications of SNS were summarized to demonstrate its potential in researching cell evolution.

Heterogeneity-mediated cellular adaptation and its trade-off: searching for the general principles of diseases

Big-data-omics have promised the success of precision medicine. However, most common diseases belong to adaptive systems where the precision is all but difficult to achieve. In this commentary, I propose a heterogeneity-mediated cellular adaptive model to search for the general model of diseases, which also illustrates why in most non-infectious non-Mendelian diseases the involvement of cellular evolution is less predictable when gene profiles are used. This synthesis is based on the following new observations/concepts: 1) the gene only codes "parts inheritance" while the genome codes "system inheritance" or the entire blueprint; 2) the nature of somatic genetic coding is fuzzy rather than precise, and genetic alterations are not just the results of genetic error but are in fact generated from internal adaptive mechanisms in response to environmental dynamics; 3) stress-response is less specific within cellular evolutionary context when compared to known biochemical specificities; and 4) most medical interventions have their unavoidable uncertainties and often can function as negative harmful stresses as trade-offs. The acknowledgment of diseases as adaptive systems calls for the action to integrate genome- (not simply individual gene-) mediated cellular evolution into molecular medicine.

Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation

BACKGROUND

Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level.

SCOPE OF REVIEW

We focus on intra-tumour heterogeneity, namely between-cell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies.

MAJOR CONCLUSIONS

Plasticity in cancer cells, i.e., partial reversal to a stem-like status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies.

GENERAL SIGNIFICANCE

Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies

BACKGROUND

Chromosome instability leads to aneuploidy, a state in which cells have abnormal numbers of chromosomes, and is found in two out of three cancers. In a chromosomal instable p53 deficient mouse model with accelerated lymphomagenesis, we previously observed whole chromosome copy number changes affecting all lymphoma cells. This suggests that chromosome instability is somehow suppressed in the aneuploid lymphomas or that selection for frequently lost/gained chromosomes out-competes the CIN-imposed mis-segregation.

RESULTS

To distinguish between these explanations and to examine karyotype dynamics in chromosome instable lymphoma, we use a newly developed single-cell whole genome sequencing (scWGS) platform that provides a complete and unbiased overview of copy number variations (CNV) in individual cells. To analyse these scWGS data, we develop AneuFinder, which allows annotation of copy number changes in a fully automated fashion and quantification of CNV heterogeneity between cells. Single-cell sequencing and AneuFinder analysis reveals high levels of copy number heterogeneity in chromosome instability-driven murine T-cell lymphoma samples, indicating ongoing chromosome instability. Application of this technology to human B cell leukaemias reveals different levels of karyotype heterogeneity in these cancers.

CONCLUSION

Our data show that even though aneuploid tumours select for particular and recurring chromosome combinations, single-cell analysis using AneuFinder reveals copy number heterogeneity. This suggests ongoing chromosome instability that other platforms fail to detect. As chromosome instability might drive tumour evolution, karyotype analysis using single-cell sequencing technology could become an essential tool for cancer treatment stratification.

Why it is crucial to analyze non clonal chromosome aberrations or NCCAs?

Current cytogenetics has largely focused its efforts on the identification of recurrent karyotypic alterations, also known as clonal chromosomal aberrations (CCAs). The rationale of doing so seems simple: recurrent genetic changes are relevant for diseases or specific physiological conditions, while non clonal chromosome aberrations (NCCAs) are insignificant genetic background or noise. However, in reality, the vast majority of chromosomal alterations are NCCAs, and it is challenging to identify commonly shared CCAs in most solid tumors. Furthermore, the karyotype, rather than genes, represents the system inheritance, or blueprint, and each NCCA represents an altered genome system. These realizations underscore the importance of the re-evaluation of NCCAs in cytogenetic analyses. In this concept article, we briefly review the definition of NCCAs, some historical misconceptions about them, and why NCCAs are not insignificant "noise," but rather a highly significant feature of the cellular population for providing genome heterogeneity and complexity, representing one important form of fuzzy inheritance. The frequencies of NCCAs also represent an index to measure both internally- and environmentally-induced genome instability. Additionally, the NCCA/CCA cycle is associated with macro- and micro-cellular evolution. Lastly, elevated NCCAs are observed in many disease/illness conditions. Considering all of these factors, we call for the immediate action of studying and reporting NCCAs. Specifically, effort is needed to characterize and compare different types of NCCAs, to define their baseline in various tissues, to develop methods to access mitotic cells, to re-examine/interpret the NCCAs data, and to develop an NCCA database.

5p13.3p13.2 duplication associated with developmental delay, congenital malformations and chromosome instability manifested as low-level aneuploidy

Recent developments in molecular cytogenetics allow the detection of genomic rearrangements at an unprecedented level leading to discoveries of previously unknown chromosomal imbalances (zygotic and post-zygotic/mosaic). These can be accompanied by a different kind of pathological genome variations, i.e. chromosome instability (CIN) manifested as structural chromosomal rearrangements and low-level mosaic aneuploidy. Fortunately, combining whole-genome and single-cell molecular cytogenetic techniques with bioinformatics offers an opportunity to link genomic changes to specific molecular or cellular pathology. High-resolution chromosomal SNP microarray analysis was performed to study the genome of a 15-month-aged boy presented with developmental delay, congenital malformations, feeding problems, deafness, epileptiform activity, and eye pathology. In addition, somatic chromosomal mutations (CIN) were analyzed by fluorescence in situ hybridization (FISH). Interstitial 5p13.3p13.2 duplication was revealed in the index patient. Moreover, CIN manifested almost exclusively as chromosome losses and gains (aneuploidy) was detected. Using bioinformatic analysis of SNP array data and FISH results, CIN association with the genomic imbalance resulted from the duplication was proposed. The duplication was demonstrated to encompass genes implicated in cell cycle, programmed cell death, chromosome segregation and genome stability maintenance pathways as shown by an interactomic analysis. Genotype-phenotype correlations were observed, as well. To the best our knowledge, identical duplications have not been reported in the available literature. Apart from genotype-phenotype correlations, it was possible to propose a link between the duplication and CIN (aneuploidy). This case study demonstrates that combining SNP array genomic analysis, bioinformatics and molecular cytogenetic evaluation of somatic genome variations is able to provide a view on cellular and molecular pathology in a personalized manner. Therefore, one can speculate that similar approaches targeting both interindividual and intercellular genomic variations could be useful for a better understanding of disease mechanisms and disease-related biological processes.

Genomic Copy Number Variation Affecting Genes Involved in the Cell Cycle Pathway: Implications for Somatic Mosaicism

Somatic genome variations (mosaicism) seem to represent a common mechanism for human intercellular/interindividual diversity in health and disease. However, origins and mechanisms of somatic mosaicism remain a matter of conjecture. Recently, it has been hypothesized that zygotic genomic variation naturally occurring in humans is likely to predispose to nonheritable genetic changes (aneuploidy) acquired during the lifetime through affecting cell cycle regulation, genome stability maintenance, and related pathways. Here, we have evaluated genomic copy number variation (CNV) in genes implicated in the cell cycle pathway (according to Kyoto Encyclopedia of Genes and Genomes/KEGG) within a cohort of patients with intellectual disability, autism, and/or epilepsy, in which the phenotype was not associated with genomic rearrangements altering this pathway. Benign CNVs affecting 20 genes of the cell cycle pathway were detected in 161 out of 255 patients (71.6%). Among them, 62 individuals exhibited >2 CNVs affecting the cell cycle pathway. Taking into account the number of individuals demonstrating CNV of these genes, a support for this hypothesis appears to be presented. Accordingly, we speculate that further studies of CNV burden across the genes implicated in related pathways might clarify whether zygotic genomic variation generates somatic mosaicism in health and disease.

Step-wise and punctuated genome evolution drive phenotype changes of tumor cells

The pattern of genome evolution can be divided into two phases: the step-wise continuous phase (step-wise clonal evolution, stable dominant clonal chromosome aberrations (CCAs), and low frequency of non-CCAs, NCCAs) and punctuated phase (marked by elevated NCCAs and transitional CCAs). Depending on the phase, system stresses (the diverse CIN promoting factors) may lead to the very different phenotype responses. To address the contribution of chromosome instability (CIN) to phenotype changes of tumor cells, we characterized CCAs/NCCAs of HeLa and HEK293 cells, and their derivatives after genotoxic stresses (a stable plasmid transfection, ectopic expression of cancer-associated CHI3L1 gene or treatment with temozolomide) by conventional cytogenetics, copy number alterations (CNAs) by array comparative genome hybridization, and phenotype changes by cell viability and soft agar assays. Transfection of either the empty vector pcDNA3.1 or pcDNA3.1_CHI3L1 into 293 cells initiated the punctuated genome changes. In contrast, HeLa_CHI3L1 cells demonstrated the step-wise genome changes. Increased CIN correlated with lower viability of 293_pcDNA3.1 cells but higher colony formation efficiency (CFE). Artificial CHI3L1 production in 293_CHI3L1 cells increased viability and further contributed to CFE. The opposite growth characteristics of 293_CHI3L1 and HeLa_CHI3L1 cells were revealed. The effect and function of a (trans)gene can be opposite and versatile in cells with different genetic network, which is defined by genome context. Temozolomide treatment of 293_pcDNA3.1 cells intensified the stochastic punctuated genome changes and CNAs, and significantly reduced viability and CFE. In contrast, temozolomide treatment of HeLa_CHI3L1 cells promoted the step-wise genome changes, CNAs, and increased viability and CFE, which did not correlate with the ectopic CHI3L1 production. Thus, consistent coevolution of karyotypes and phenotypes was observed. CIN as a driving force of genome evolution significantly influences growth characteristics of tumor cells and should be always taken into consideration during the different experimental manipulations.

Evolutionary mechanism unifies the hallmarks of cancer

The basis for the gene mutation theory of cancer that dominates current molecular cancer research consists of: the belief that gene-level aberrations such as mutations are the main cause of cancers, the concept that stepwise gene mutation accumulation drives cancer progression, and the hallmarks of cancer. The research community swiftly embraced the hallmarks of cancer, as such synthesis has supported the notions that common cancer genes are responsible for the majority of cancers and the complexity of cancer can be dissected into simplified molecular principles. The gene/pathway classification based on individual hallmarks provides explanation for the large number of diverse gene mutations, which is in contrast to the original estimation that only a handful of gene mutations would be discovered. Further, these hallmarks have been highly influential as they also provide the rationale and research direction for continued gene-based cancer research. While the molecular knowledge of these hallmarks is drastically increasing, the clinical implication remains limited, as cancer dynamics cannot be summarized by a few isolated/fixed molecular principles. Furthermore, the highly heterogeneous genetic signature of cancers, including massive stochastic genome alterations, challenges the utility of continuously studying each individual gene mutation under the framework of these hallmarks. It is therefore necessary to re-evaluate the concept of cancer hallmarks through the lens of cancer evolution. In this analysis, the evolutionary basis for the hallmarks of cancer will be discussed and the evolutionary mechanism of cancer suggested by the genome theory will be employed to unify the diverse molecular mechanisms of cancer.

Stress, genomic adaptation, and the evolutionary trade-off

Cells are constantly exposed to various internal and external stresses. The importance of cellular stress and its implication to disease conditions have become popular research topics. Many ongoing investigations focus on the sources of stress, their specific molecular mechanisms and interactions, especially regarding their contributions to many common and complex diseases through defined molecular pathways. Numerous molecular mechanisms have been linked to endoplasmic reticulum stress along with many unexpected findings, drastically increasing the complexity of our molecular understanding and challenging how to apply individual mechanism-based knowledge in the clinic. A newly emergent genome theory searches for the synthesis of a general evolutionary mechanism that unifies different types of stress and functional relationships from a genome-defined system point of view. Herein, we discuss the evolutionary relationship between stress and somatic cell adaptation under physiological, pathological, and somatic cell survival conditions, the multiple meanings to achieve adaptation and its potential trade-off. In particular, we purposely defocus from specific stresses and mechanisms by redirecting attention toward studying underlying general mechanisms.

Unstable genomes elevate transcriptome dynamics

The challenge of identifying common expression signatures in cancer is well known, however the reason behind this is largely unclear. Traditionally variation in expression signatures has been attributed to technological problems, however recent evidence suggests that chromosome instability (CIN) and resultant karyotypic heterogeneity may be a large contributing factor. Using a well-defined model of immortalization, we systematically compared the pattern of genome alteration and expression dynamics during somatic evolution. Co-measurement of global gene expression and karyotypic alteration throughout the immortalization process reveals that karyotype changes influence gene expression as major structural and numerical karyotypic alterations result in large gene expression deviation. Replicate samples from stages with stable genomes are more similar to each other than are replicate samples with karyotypic heterogeneity. Karyotypic and gene expression change during immortalization is dynamic as each stage of progression has a unique expression pattern. This was further verified by comparing global expression in two replicates grown in one flask with known karyotypes. Replicates with higher karyotypic instability were found to be less similar than replicates with stable karyotypes. This data illustrates the karyotype, transcriptome, and transcriptome determined pathways are in constant flux during somatic cellular evolution (particularly during the macroevolutionary phase) and this flux is an inextricable feature of CIN and essential for cancer formation. The findings presented here underscore the importance of understanding the evolutionary process of cancer in order to design improved treatment modalities.

Current challenges in the bioinformatics of single cell genomics

Single cell genomics is a rapidly growing field with many new techniques emerging in the past few years. However, few bioinformatics tools specific for single cell genomics analysis are available. Single cell DNA/RNA sequencing data usually have low genome coverage and high amplification bias, which makes bioinformatics analysis challenging. Many current bioinformatics tools developed for bulk cell sequencing do not work well with single cell sequencing data. Here, we summarize current challenges in the bioinformatics analysis of single cell genomic DNA sequencing and single cell transcriptomes. These challenges include calling copy number variations, identifying mutated genes in tumor samples, reconstructing cell lineages, recovering low abundant transcripts, and improving the accuracy of quantitative analysis of transcripts. Development in single cell genomics bioinformatics analysis will promote the application of this technology to basic biology and medical research.

Genome chaos: survival strategy during crisis

Genome chaos, a process of complex, rapid genome re-organization, results in the formation of chaotic genomes, which is followed by the potential to establish stable genomes. It was initially detected through cytogenetic analyses, and recently confirmed by whole-genome sequencing efforts which identified multiple subtypes including "chromothripsis", "chromoplexy", "chromoanasynthesis", and "chromoanagenesis". Although genome chaos occurs commonly in tumors, both the mechanism and detailed aspects of the process are unknown due to the inability of observing its evolution over time in clinical samples. Here, an experimental system to monitor the evolutionary process of genome chaos was developed to elucidate its mechanisms. Genome chaos occurs following exposure to chemotherapeutics with different mechanisms, which act collectively as stressors. Characterization of the karyotype and its dynamic changes prior to, during, and after induction of genome chaos demonstrates that chromosome fragmentation (C-Frag) occurs just prior to chaotic genome formation. Chaotic genomes seem to form by random rejoining of chromosomal fragments, in part through non-homologous end joining (NHEJ). Stress induced genome chaos results in increased karyotypic heterogeneity. Such increased evolutionary potential is demonstrated by the identification of increased transcriptome dynamics associated with high levels of karyotypic variance. In contrast to impacting on a limited number of cancer genes, re-organized genomes lead to new system dynamics essential for cancer evolution. Genome chaos acts as a mechanism of rapid, adaptive, genome-based evolution that plays an essential role in promoting rapid macroevolution of new genome-defined systems during crisis, which may explain some unwanted consequences of cancer treatment.