Figure
1.
Composition of human coding and non-coding DNA[8].
| Citation: |
Gradimir Misevic. Single cell human genomic analyses: a way to refine the knowledge of cellular heterogeneity origins in individual subject[J]. Blood&Genomics, 2021, 5(2): 83-96. DOI: 10.46701/BG.2021022021112
|
Genomic analyses are commonly done on the population of several millions of cells. The main reason is that the most frequently used sequencing procedures require sufficient amounts, usually milligrams, of DNA for fragmentation, cloning, and amplification of obtained cDNA libraries in order to cover the whole genome length[1]. Such a quantitative genetic approach during the past three decades has resulted in the assembly of the complete genome sequences of humans, as well as in many other species that are constantly being improved and updated[2–7]. The last assembled reference genome was designated GRCh38. Several coding and non-coding parts of DNA have been quantitatively characterized and accordingly classified[8] (Fig. 1).
The speed of sequencing on cell population samples derived from a single or several individuals was dramatically increased and now eleven human genomes were sequenced and assembled in nine days[9]. However, DNA sequencing using such a population of cells always provides a map of an average genome and does not allow the recognition of possibly existing genomic variation between individual cells. In order to understand the role of human DNA sequence changes during the cellular ontogeny of over 30 trillion cells with about 400 different cell types of distinguishable phenotypes, versus selective gene expression, both being possible driving forces for cellular heterogeneity, the research in genetics and molecular biology during the last decade and currently ongoing is focusing on the single cell genomic analyses[1,10]. This approach, which relies on the complete DNA sequencing of individual cells, has the goal of refining our knowledge of cellular heterogeneity origins. Here, single cell genomic analyses will be reviewed by presenting technological and scientific achievements that provide evidence for the existence of genetic heterogeneity arising through mutations, deletions, recombinations, and duplications of DNA sequences in human diploid somatic, and haploid germline cells, that define so far invisible phenotypes.
The healthy adult human organism at 70 kg is estimated to have about 3.72×1013 cells and about an equal number of bacterial cells with a total weight of 0.2 kg[11–12]. Currently, about 400 different cell types have been identified in non-diseased human subjects[11–13]. Hematopoietic cells represent 90% of the total population and are dominated by 84% (2.5×1013) red blood cells, 4.9% platelets, 2.9% bone marrow cells, and 1.5% lymphocytes (Fig. 2A). With the exception of vascular endothelial cells contributing 2.1% to the total cell number, blood cells are dominating the cellular content of the human body.
Integration of available data obtained by numerous measurements of cell volumes and cell numbers for all different cell types and bacteria present in the human body showed that muscle and adipocytes cells are major contributors of the body mass, whereas erythrocytes being small contribute very little mass in spite of being the most abundant with the number of cells[11–12] (Fig. 2B).
Genetic, epigenetic, and stochastic factors govern the cellular heterogeneity of the 400 different cell types that arise during embryonal development and adult life[11–13]. Studying the molecular nature of the cellular diversity phenomenon was usually conducted by using: a) classical cellular morphology combined with immunohistology, and in vivo and in vitro functional tests, or, b) biochemical and genetic analyses of tissue isolates, such as micro biopsies containing usually more than millions of cells. Employment of the first approach reveals general information about cellular anatomic differences with limited information about the molecular content of observed single cells, whereas the second approach delivers more detailed data concerning the molecular content. However, these are of an average, rather than an analysis of every individual cell within the examined populations. Besides being conducted on the population of cells, molecular omic analyses are unfortunately also partial and are restricted to separate analyses for each of the main classes of biomolecules, such as nucleic acids (DNA and RNA), proteins, glycoproteins, glycans, lipids, glycolipids, and metabolites. These limitations are due to the inability of currently available technologies to reach the sensitivity of single molecular detection for each type of biomolecules[14] (Fig. 3). In order to study the molecular basis of cellular heterogeneity and its associated phenotypes, numerous single cell analysis approaches have begun and have been reviewed for DNA[1] and RNA[15–19], and other biomolecules[14].
Sources like the human atlas project are included in their cell types catalog a large number of cell lines which are not naturally occurring. They are changed through genetic and/or epigenetic manipulation and can be further modified in an enormous and steadily growing number of cell types with different genomes. This review is focused on cellular genomics of human body and their naturally occurring physiological and pathologically caused variability, and not modified and adapted cells which exist only in cell culture. In conclusion, as stated above currently there are about 400 cell types identified in humans.
Nowadays, all forms of single cell analyses are still not able to provide a complete molecular content for each individual cell[14]. However, valuable genomic, transcriptomic, proteomic glycomic, lipidomic, and metabolomic data have been collected and are starting to improve our basic knowledge about the types of molecular diversity underlying the previously observed and/or newly discovered cellular heterogeneities expressed as different functional phenotypes. In particular, the single cell transcriptomic field has over 500 publications containing curated data[15], which are available from the "Human Cell Atlas"[16], and single cell RNA sequence Data Base (scRNAseqDB)[17] connected with the Gene Expression Omnibus[18]. RNA sequencing is performed using a similar technology to nucleic acid copy number amplification, but also in few cases by in situ and by microarray approaches. Commonly, Drop-seq, and now more increasingly Chromium, InDrops Smart-seq2, and other more than 100 approaches are used[19]. The number of individual cells analyzed varies in different studies from 10 to 10×105. Due to the excellent methodology of amplification, the detection limit is reaching single molecular sensitivity with the possibility of identifying hundreds to thousands of mRNA species. The "Single Cell Type Atlas" platform presented the assembled single cell transcriptomic data collected from 192 individual cell types representing 12 different cell type groups from 13 different human tissues, and correlated them with immunohistochemical analyses of protein expression[20]. In spite of the great progress in all single cell sub omic fields, according to available published information, the assembly of the complete molecular content for all classes of biomolecules has still not been achieved. Further development of single cell analyses technology is necessary to fulfill the essentially needed high throughput systems for the parallel processing of millions of cells and single molecular detection[14].
Curated information obtained from numerous genomic experiments usually done on the population of cells present in different human tissues revealed that the total number of genes code 19 628 possible proteins, of which 7367 are expressed in all tissues as housekeeping proteins, while the rest are more tissue specific[21]. However, this collective information on cell populations does not provide knowledge about the genome, transcriptome, proteome, or other molecular omes heterogeneity at the single cell level[14]. It is therefore very important to decode to what extent the genotypes of individual cells may be different, and how they contribute together with selective gene expression to: a) cell differentiation during embryogenesis; b) the functioning of tissue specific cell types in the adult organism (e.g. neural, immune, and any other organ systems); c) tissue renewal and regeneration (e.g. hematopoietic, epithelial, endothelial, and liver); d) pathological conditions specifically related to oncogenic transformation and metastasis of tumor cells.
It is important to recognize that there is a difference between the number of genes and the number of RNAs and proteins present within cells. RNAs and proteins tremendously exceed the number of genes. This review focuses on genetic variability as a way to refine the knowledge of cellular heterogeneity origins and not of gene expression of RNA and proteins present within cells with the same genome.
As outlined in the above section "Number and types of cells in the human body", so far 400 different cell types have been identified[11–12]. Since analyses used to categorize these cell types were mostly done on cell populations (not on single cells), and since they were also lacking complete and single molecular detection sensitivity, the question remains whether each of these cell types is heterogeneous in terms of their biomolecular content that may have remained invisible due to current technological limits. Furthermore, temporal factors such as changes occurring in subpopulations during the life cycle must also be considered. The causes of any type of cellular heterogeneity within a single human individual can be divided, with respect to genomic analyses, into two broad categories: 1) cells that remain with a constant genome sequence, and 2) cells that change their genome sequences.
Within the first category of causes of cellular heterogeneity, where cells keep a constant genome, three possible mechanisms leading to cell diversity can be envisaged. The first is based on the programmed spatial and temporal control of gene expression during embryonal cell differentiation, and throughout adult life cell recycling and regeneration. The underlying molecular mechanism is nowadays a common knowledge well documented in textbooks of developmental and molecular biology. However, such a presence of the equivalent genome in each cell has so far not been confirmed at the single cell level. Since great efforts invested in procedures to dedifferentiate all cell types and make them pluripotent as stem cells were practically unsuccessful, the mechanism of programed gene expression seems until now to result in stable cell states that are difficult to change, according to the currently available knowledge. The second possible mechanism takes into account environmental influences, such as temperature, pressure, electromagnetic radiation, pH, salinity, availability of energy, and quantity and quality of food sources. These epigenetic factors can lead to physico-chemical changes that act through reprogramming the temporal pattern of gene expression by various biochemical reactions known today such as DNA methylation, histone modification, and non-coding RNA alterations[22–29]. These new states of gene expression would result in the generation of an additional variety of phenotypes that may be visible only by following the appearance of appropriate bimolecular markers using single cell analyses with high detection sensitivity[22]. Epigenetics induce changes that are usually physiologically the most appropriate response, however in hazardous conditions induce less controlled actions leading to various pathological states. Depending on the level and duration of exposure to external factors, many epigenetic changes were shown to progressively develop during a life cycle. However, they also may reverse in the case of the restoration of environmental conditions. Speaking of single cell analyses, new approaches for assessing the epigenetic influence on cellular heterogeneity are being established[30]. Finally, the third mechanism resulting in cellular heterogeneity is based on stochastically and thermodynamically possible chemical and biochemical changes that can be theoretically assumed to occur under constant environmental conditions. Providing experimental evidence for this mechanism requires detailed single cell analyses and statistical evaluations from separate experiments under strictly monitored and controlled experimental settings.
Within the second category of cellular heterogeneity caused by changes in genome sequences in individual human cells, one of the possible scenarios that should not lead to morphological and molecular phenotypic changes involves the case of genetic mutation occurring either in the non-coding or non-regulatory DNA sequences. Such genotype changes are invisible and do not result in alteration of cellular functions. If the genome is changed in any way to modify the gene expression, thus altering any molecular structure and function within a cell, three possible mechanisms resulting in cellular heterogeneity can be considered to occur. The first is based on temporally programmed changes of gene sequences. These are exemplified by mutations and the recombination of immunoglobulin and T receptor genes during the generation of humoral immunity of B and T cells occurring through the entire life of healthy human individuals. The result is the generation of uncountable clonal heterogeneity of lymphocytes. For this research, several Nobel Prizes were awarded[31–33]. Such immune related genomic changes are controlled by means of restricting them to a specific genome region. However, it seems that modifications in nucleotide sequence are a stochastically driven process, followed by a strictly controlled selection of non-self-reacting cells. Furthermore, lymphocytes are constantly renewed, and new types of diversity continues to arise throughout the entire life cycle. The second type of genetic change is the environmentally induced alteration of DNA sequences by toxic and/or highly reactive chemicals, and electromagnetic and particle radiations that are well studied and documented in many molecular biology and biochemistry books. They may remain permanent in somatic, as well as in germ cells if the repair machinery is not capable of recognizing them. One of the most extreme genetic changes is aneuploidy, defined as whole chromosome or chromosome-arm imbalance. It was shown that aneuploidy heterogeneity occurs in 88% of cancers with pattern alterations that are tumor-type specific[34–36]. Aneuploidy correlates with cell-cycle genes and anti-correlates with immune levels. The single cell genomic research on this type of genotype changes related to cellular heterogeneity is starting to be more extensively studied in human tumors. The third molecular mechanism, based on the stochastic process of paring complementary nucleotides during DNA replication, can also cause cellular heterogeneity by changing genome sequences in the case of not being recognized and reconstituted by the repair enzymatic system. The result may lead to a generation of persisting cellular heterogeneity without the influence of external factors. Such stable mutations can potentially cause pathological conditions such as the oncogenic transformation of individual cell clones.
In the field of genetically driven cellular heterogeneity, an open unanswered question is: how does the stem cell preserve the non-mutated DNA chain as the genetic source during division? The "immortal strand hypothesis" theory suggests the mechanism of non-random segregation, that is challenged by different opinions, and is thus still missing the definitive proof [37].
The current goal of single cell research is to better understand cellular heterogeneity and its associated phenotypes through the analyses of the complete molecular content of each individual cell present in the tissue sample. Such a single cell approach requires the development of several challenging technological solutions, dealing with: a) the parallel processing of millions of individual cells in a minute volume, and b) single molecule detection and identification sensitivity for all types of biomolecules, DNA, RNA, proteins, glycans, lipids metabolites, and inorganic molecules within each of over a million analyzed cells. For DNA and RNA, this means a complete sequencing of the entire genome and all RNA species present in each individual cell. Unfortunately, numerous reports in the past two decades on single cell genomic, transcriptomic, proteomic, glycomic, lipidomic, and metabolomic analyses are still providing only partial but somewhat valuable information about cellular heterogeneity in a human body, whereas even more extensive studies on the cell population are offering information only limited to the average cell[14].
Genomic analysis was and is still commonly done on the population of 105 to 106 cells from a single individual or from a relatively small group of people[2–6,9,38–39]. The main reason is to obtain a sufficient number of DNA copies necessary for reliable and complete genome sequencing. Therefore, results from such analyses are providing information of the average genome and are not allowing for the recognition of genomic variation either between individual cells within a single human subject, or genetic diversity at the level of single cells within the human population. Recently a single-cell genomic consortia was created[40].
A survey of the published research on quantitative single cell genomic analyses in humans revealed the substantial increase in the number of analyzed cells from only a few cells previously, to a now much higher throughput that is ranging from a few hundreds to thousands of cells. The general approach used for single cell human genomics, and notable examples of whole and partial genome sequencing performed on single cells derived from human primary tumors, circulating tumor cells, tumor cells after drug treatment, tumor cell lines, sperm cells, and neurons, will be summarized.
All human cells, with the exception of mature red blood cells and platelets, contain genetic information coded in the sequence of DNA. Each diploid cell has 23 pairs of double strand DNA polymers built by approximately 3×109 complementary nucleotide pairs[41] (Fig. 3). If stretched, human chromosomal DNA would have a length of approximately 2 meters. Complete genomic analyses require sequencing of the entire length of DNA. In order to achieve the necessary detection of each individual nucleotide present in the entire DNA sequence of each individual cell, current technologies utilize amplification of the number of nucleic acid copies present in a single cell[1,42]. Although single cell whole genomic sequencing currently shows good progress in a few particular examples, the errorless amplification of about 6 to 12 picograms of DNA per cell remains methodologically very challenging[43–44,19]. Enzymatic copying of such a small amount of DNA has to allow 100 percent errorless constructions of complete genome libraries and their sequencing. At least thousands of such libraries have to be separately prepared from each individual cell nuclei present in a sample in order to sequence all genomes, including ones of rare occurring cells. This means that differences between every single cell in the population can be evaluated. Often, single cell genomics does not implement whole genome sequencing, but rather focuses on the partial sequencing of specific genomic elements. Both complete and partial genomic approaches allow for comparison between genomes of a large number of single cells in multicellular organisms. This provides the possibility to better understand the existence and functional importance of genetic variations and genomic heterogeneity in somatic and germ cells, as well as those cells rapidly dividing in embryos, in regenerating tissues, in blood cells hematopoiesis, and tumors.
The first procedural step for single cell genomic analyses entails the preparation and isolation of individual cells from a sample of human tissue. The low-cost manual and slow serial procedures are based on micromanipulation, which requires higher skills. Automated and higher-cost micromanipulation is therefore preferred for the analyses of a large number of cells. The common alternatives are fluorescence or magnetic activated cell sorting, laser capture microdissection, and microfluidics[45–46]. The second step requires isolation and amplification of DNA from each separated single cell[42,47–50]. This can be achieved using several procedures. The degenerative oligonucleotide PCR method permits uniform amplification with relatively low coverage of the genome. This can be used for detecting copy number variation, but not for measuring single nucleotide variation. The multiple displacement amplification method has reverse characteristics to the previous method, due to its lower uniformity but high coverage of the genome and its inherent ability to measure single nucleotide variation but not for detecting copy number variation. The alternative method is the micro-well displacement amplification system, which offers the possibility of decreasing the volume. Multiple annealing and looping-based amplification cycles (MALBAC) is a suitable method for measuring copy number variation and single nucleotide variation with high genome coverage and high uniformity of amplification. The Nuc-seq is actually modified multiple displacement amplification, which entails the selection of G2/M nuclei in order to reduce allelic dropout and the false-positive rate. It is suitable for whole genome sequencing. In addition, in 2017 a scalable single cell library preparation of the whole genome without preamplification was reported[51].
The first attempt of single cell genomics covered about 10% of the total DNA sequence. In the past 10 years, excellent progress in methodology has resulted in over 90% genome coverage in each analyzed cell. Interesting results showing genetic diversity and clonal linage were obtained in several genomic single cell analyses of primary human tumors originating from the breast[10,34,42,52–54], bladder[55], kidney[56–57], liver[58], myeloproliferative JAK2 negative cells[59], acute myeloid leukemia[60–62], glioblastoma[63], acute lymphoblastic leukemia[64], and lung cancer[65], as well as of circulating myeloma and tumor cells originating from the breast cancer, prostate caner[66–67], colon caner[68], and lung cancer[69].
Major methodological advances for single cell genomic analyses have occurred during the past two decades. They use novel technological approaches for: a) single cell isolation; b) DNA isolation and amplification; c) sequencing; and d) genome analyses and assemblies (Fig. 4).
Single cell isolation can be performed using four different methodological approaches[45–46]. The first one is by inexpensive manual micromanipulation that is usually time consuming and not very precise, thus hindering the preparation of large numbers of cells. The second type is robotic micromanipulation that is high in cost but faster and is a more precise way of preparing larger numbers of cells. The third methodology is based on fluorescence activated cell sorting (FACS) and is also high cost, but most suitable for the isolation of large numbers of cells. Similarly magnetic activated cell sorting (MAGS) is high cost, but also suitable for isolation of large numbers of cells. However, cell sorting is limited to cell surface markers. Microfluidics is a high throughput and medium cost method that can be adapted with various configurations and allows for the microscopic monitoring of cells during separation and isolation of individual sells. The last methodological approach for single cell isolation is high cost and high throughput laser capture microdissection (LCM). Caution must be taken when using this method for single cell genomic analyses because of applied high power lasers that can damage nucleic acids.
Single cell DNA amplification methods can be classified into five different approaches that are based on degree of genomic coverage, uniformity of amplification, and reaction volumes[47,49–50]. Degenerative oligonucleotide PCR (DOP-PCR) is suitable for highly uniform amplification, however has low genome coverage. This method is therefore commonly used for copy number variations (CNVs) analyses but not for single nucleotide variations (SNVs) detection. The second commonly used method is multiple displacement amplification (MDA), which allows for high precision and high coverage of genome analyses, but will not permit uniform amplification. Thus, this approach complements DOP-PCR since it allows SNV detection rather than CNV detection. In order to minimize the mistakes with allelic readings leading to dropouts and false-positive rates, Nuc-seq was introduced as an altered MDA procedure that applies flow-sorting for the selection of G2/M nuclei[49]. The fourth amplification approach, known as micro well displacement amplification (MIDAS), was developed for the purpose of reducing the reaction volume and increasing high throughput that permits uniform amplifications. The fifth method type is MALBAC. This approach joins quasi-linear pre-amplification by means of applying strand displacement active polymerase. By employing MALBAC, over 90% genome coverage can be achieved with the efficiency of almost 80% SNV detection. Thus, this method is appropriate for CNV and SNV detection, providing a minimal (<1%) allelic drop-out rate and minimal (4×10−5) false-positive rate.
For single cell sequencing and quantification of DNA next generation sequencing (NGS), nanopore sequencing, and DNA microarrays are used for whole genome amplification (WGA) of single cells together with computing analytical tools. For more detailed information of these now commonly used technologies, visit: https://www.illumina.com/systems/sequencing-platforms.html; https://nanoporetech.com/how-it-works; https://www.ncbi.nlm.nih.gov/projects/genome/guide/human/index.shtml.
The last step in single cell genomic analyses is genome assembly and the use of computational analyses. Initial computation is done by mapping data to a reference genome. Many algorithms using different approaches have been developed which would take a text book to review. Therefore only a brief citing of few approaches related to single cell analyses will be mentioned here.
Single cell algorithm for reconstructing loss-supported evolution of tumors (SCARLET) was developed for single cell tumor phylogeny inference with copy number constrained mutation losses[70]. It is accepted that a small number of somatic mutations can cause the development of cancer, therefore, the detection of such mutations in single tumor cells can be used as markers of evolutionary tumor history. Since copy number aberrations (CNAs) can overlap with SNVs, thus leading to SNV losses, the use of the SCARLET algorithm for a single cell DNA sequencing data can overcome such problems. Authors published an example using a single cell dataset from a patient with colorectal cancer to establish tumor phylogeny that can be related to tumor growth and metastasis. SCARLET is available at: github.com/raphael-group/scarlet. Another approach is normalization and copy-number estimation method (SCOPE) for WGA single cell DNA Sequencing[71]. RobustClone is yet another additional computational method for tumor clone and evolution inference from single cell sequencing data[72]. It can be applied for the analysis of SNV and CNV data. Authors reported the spatial progression patterns of subclonal evolution on large-scale 10X Genomics CNVs in breast cancer dataset.
Single cell barcoding has gained popularity for using in parallel higher throughput genomic analyses. However, this approach has low genome coverage and consequentially does not allow for the precise determination of allele- and haplotype copy number. In order to improve the analyses of such data, CHISEL was used for the determination of allele-specific mutations in ten single cell sequencing datasets with 2000 cells that were obtained from two breast cancer patients[73]. Allele specific CNAs in these samples, including copy-neutral, whole-genome duplications and mirrored-subclonal CNAs were shown to affect genomic regions containing breast-cancer genes, as well as evidence of convergent evolution.
Another recently published approach for identifying tumor clones in sparse single cell mutation data from whole genome sequencing with sequencing coverage below 0.5 per cell was named SBMClone[74]. This bioinformatic approach enabled better CNAs and single nucleotide mutations analyses in individual cells. SBMClone was used for the evaluation of single cell whole genome sequencing data from two breast cancer patients. Analyses of data from the first patient recuperated the major clonal composition after 10X Genomics CNV sequencing with 0.03X coverage. In the case of the second patient, SBMClone data analyses of sequences obtained by DOP-PCR with a coverage of 0.5X showed that tumor cells are present in the post-treatment sample, which was not seen in the previous analysis of the same dataset.
The purpose of this section is to review a selected set of publications which have demonstrated fundamental findings about genomic variability in single cells from healthy and tumor cells of individual human subjects.
In 2011, one particular pioneering study showed the presence of clonal evolution in human breast cancer by the analyses of hundreds of single cells obtained from two patients. These results supported a punctuated example of copy number evolution in these types of tumors[42]. Further single cell genomic analyses of samples isolated from breast cancer in two patients revealed that the time of genetic copy number transformation occurs early in malignant transformation as punctuated bursts[10,34,42]. Interestingly this process of genotype changes was later followed by progressively developing a variety of point mutations resulting in the widespread clonal diversity of breast cancer. Also very rare mutations were found in some subclones[10,34,42,49]. This set of studies indicated the great importance of large-scale single cell analyses in human breast tumors that have implications in diagnosis as well as in decision for anti-cancer therapies. Nine hundred single-cell DNA sequencing showed the evolution of chemoresistance in triple-negative breast cancer in 8 patients. Resistant genotypes to neoadjuvant chemotherapy were preexisting and adaptively selected by this treatment[53]. The latest studies on genomic profiling of ductal carcinoma in situ of the breast showed the presence of heterogeneous populations[54]. Analyses in a 21-patient discovery cohort and targeted deep sequencing analysis in a 72-patient validation cohort revealed the existence of genetically heterogeneous cell populations related to the mutation of specific genes that can be used as predictive and therapeutic markers.
Single cell exome sequencing of human kidney tumors showed single nucleotide mutations that are characteristic for this organ[56]. Using the same approach of exome sequencing of human myeloproliferative JAK1 negative cells, a similar phenomenon of monoclonal mutation evolution was observed in 58 cells[59]. Although these studies have shown the evolution of tumor cells from a common origin, high error rates during sequencing by applied methodology prevented the construction of the detailed linage and the complete clonal substructures. Further reported analyses of single cell exomes sequencing of human bladder and colon cancer also discovered the great variety of genetically different tumor cells that were shown to have diverged from a commonly mutated clone in each tumor[55]. When following just a single gene mutation in human glioblastoma, a variant heterogeneity based on convergent evolution of epidermal growth factor receptor mutations with rearrangements in different subclones from the same primary tumors, was found through single nucleus sequencing of single tumor cell[63].
In renal medullary carcinoma, somatic alterations were reported using haplotype-resolved germline analyses[57]. This carcinoma is a rare kidney cancer in African adolescents and young adults. Genomic analyses in 15 unrelated patients diagnosed with medullar kidney carcinoma were done by linked-read genome sequencing for germline and somatic haplotypes. Reported results suggested that renal medullary carcinomas do not ascend from a single founder population and that hemoglobin S allele is the risk for this disease.
Clonal variations in DNA sequences of human tumor cells were also studied using single cell genomics on cells isolated from metastatic lesions. Such single cell analysis was performed on samples obtained from patients diagnosed with small-cell lung cancer. Reported results revealed copy number alterations with mono- and polyclonal metastatic patterns in the liver. These cells had a distinct single nucleotide variation pattern with the lowest heterogeneity among all metastatic lesions[75]. Other recent pilot studies on breast cancer showed that exome and whole genome sequencing of circulating tumor cells versus single metastatic biopsies display differences in respect to genomic changes such as the level of somatic nucleotide variations, copy number alterations, and structural variants with inter-chromosomal rearrangements, recurrent gene fusions, and tandem duplications[76–77].
A study into the relationship between intratumor heterogeneity and the associated morphologic/histological characteristics of hepatocellular carcinoma was performed by single-cell whole-genome sequencing 96 tumor cells obtained from three patients, 30–36 per each patient, and 15 normal liver cells, 5 cells per patient. The obtained hepatocellular carcinoma cells had associated hepatitis B virus. Copy number variations in these examined individual cells were revealed to develop in early stages, but remain fairly steady during progression. Both, monoclonal or polyclonal origins of cells were shown to exist and were associated with distinct morphology/histology characteristics[58].
Besides the studies conducted on solid tissues, primary tumor single cell genomic analyses were also performed in bloodborne malignant cells. Mutation order and rates were followed in three patients suffering from acute myeloid leukemia[60–61]. Another study using deep-exome sequencing of each of 1479 cells obtained from six patients diagnosed with acute lymphoblastic leukemia was applied in order to identify mutations by means of custom designed PCR primers for multiplexed and targeted sequencing. Authors reported the identification of early ETV6-RUNX1 translocations followed by multiclonal evolution[64]. Clonal evolution of acute myeloid leukemia was recently revealed by high-throughput single-cell genomics by showing mutational histories of 123 acute myeloid leukemia patients. Cooccurrence of mutation level in single cells enabled reconstruction of histories that revealed linear and branching patterns of clonal evolution[62].
An interesting publication on human multiple myeloma obtained cells from 203 patients' blood was able to detect genetic heterogeneity residing in significantly mutated genes and copy number alterations that were found in putative tumor suppressor genes by determining homozygous deletions and loss of heterozygosity[66]. A more recent study on the progression of multiple myeloma revealed the co-evolution of tumor and immune cells[78]. This single cell analyses that included CNV DNA and RNA showed that the copy number in B and plasma cells in 17 out of 21 samples had deletion of chromosome 13. The loss of chromosome 13 was revealed to be linked to aggressive malignancy. Analyses of samples from multiple patients presented large variability[78]. Recently updated work on the advancement and extension of single cell genomics analyses by single cell multi omics also confirmed genomic heterogeneity[65,69]. Another interesting integrative single cell analysis of alterations of allele specific copy number and chromatin accessibility presented DNA sequencing data from gastric, colorectal and breast cancer samples[79]. The authors revealed complex, multiallelic copy number aberrations and concluded that the existence of genetic instability and chromatin remodeling is a part of tumor evolution[79]. Particularly the latest review on single cell genomics in human lung tumors presented a summary of published results that revealed a significant heterogeneity of somatic mutations among individual cells[65].
The latest report in Genome Research journal described a single cell tumor immune atlas for precision oncology[80]. The published data sets from 217 patients, including over 500 000 cells of immune, stromal, and cancer cells in tumor sections of 13 cancer types were analyzed. The results including transcriptomics showed the possibility for better patient classifications grounded on immune cell compositions.
Taken together, all of the reported results on single cell genomics research in humans suggest alterations of the genome with clonal evolution in many primary solid tumors and blood tumors. Therefore, the conclusion could be made that a clonal expansion of tumor cell lineages having a common set of founder mutations undergo continued proliferation, resulting in tumor growth. This is in contrast to other theories suggesting cancer stem cell as origin, mutagenic field effects, and multi cell origin, because they would result in an independent cell linage without common mutations.
The MALBAC method for measuring copy number variation and single nucleotide variation, which allows high genome coverage and high uniformity of amplification, revealed a mutagenic rate of 2.5 nucleotides per cell division in the case of human colon carcinoma cell line[47]. This rate is slightly higher than that measured in non-transformed cells. In human breast tumors, the nuc-seq approach showed that the mutation rate in triple-negative breast cancer has eight mutations per cell division, which is in contrast to normal mutation rates per cell division in estrogen receptor positive cells. Further studies have to be done on large cell populations from many different tumors and many different patients before any conclusions can be made.
Besides single cell genomics in human tumors, reports on genome analyses of individual neuronal and sperm cells were also reported. Interestingly, single cell genomics on human brain cells identified clonal somatic copy number variation[81]. This study analyzed de novo copy-number variants that can actually cause neuropsychiatric diseases. Single cell whole genome sequencing of about 200 individual neuron cells obtained from three healthy and two pathological human brains identified germline trisomy of chromosome 18, but found over 95% of neurons in normal brain tissue to be euploid. Analysis of a patient with hemimegalencephaly, associated with a copy number variants of chromosome 1q, also found tetrasomy in about 20% of neurons, suggesting that these changes in a minority of cells can cause widespread brain dysfunction. Identification of over 1 megabase large clonal copy number variants in human brain tissue neurons implies the evolution of genetic changes during neurogenesis. This study concluded that many neurons obtained from both normal and diseased brains contained a few copy number variants, including one at chromosome 15q13.2–13.3, a site of duplication that was found in neuropsychiatric conditions. Other studies also reported variation of chromosomal and respective DNA number of copies in mosaic like fashion which specified clonally related differences and detectible but low level of aneuploidy[82–83]. These reports on single neuron sequencing obtained from endogenous human frontal cortex revealed that: a) 13% to 41% of cells have at least one megabase-scale de novo copy number variation, b) deletions are twice as common as duplications, and c) a subset of neurons have highly abnormal genomes manifested by many alterations. Besides the neural tissues, whole chromosome copy number alterations were also found in the liver and the skin[84]. However, this study reported that aneuploidy occurrence in the liver and brain are much less frequent than in previous reports. The authors suggest that: a) the aneuploidy alterations across mammalian tissues may not have a positive role for organ functions as some theories have suggested; and b) cancer is an example of functional adaptation.
Genome wide sequencing of human single sperm cells showed recombination activity and de novo mutation rates with distinct characteristics[85]. This study was performed on 91 single cells in order to create personal recombination that provided evidence for gene conversion. High-throughput sequencing on 31 single cells revealed the frequency of large-scale genome instability, and deeper sequencing of 8 single cells revealed de novo mutation rates.
Detailed single cell genomic studies in a variety of solid and blood cell human tumors revealed that: 1) during cell division and cell cycles, the genomic cellular diversity arises through mutations, deletions, recombination, and duplications of DNA sequences, as well as aneuploidy; 2) genetic changes in each individual human subject are based on clonal evolution leading to malignant growth, invasiveness, and metastasis; and 3) the effect of genetic diversity within the neural cell of a single individual human is not clearly understood.
Prospective research in single cell genomics should be improved by: 1) providing complete and errorless DNA sequence analyses, not only of selected parts of the genome; 2) examination of many more samples that contain over millions of cells, not only thousands, from all healthy and tumor human tissues. Futhermore, answers to following questions are needed: 1) How heterogenous is the genome of each individual cell in a single human subject? 2) How much does genomic heterogeneity contributes to cellular heterogeneity in addition to selective gene expression and epigenetic factors? 3) What are the markers for visualization of associated phenotypes that may be hidden when using classical observation and analyses? Such researches will provide more representative data about the role of genetic changes versus epigenetic and stochastic factors' contributions to cellular diversity of over 30 trillion of cells in the human body of each individual.
To Emanuela Garbarino for useful discussions and helping with writing the manuscript and literature. To GNM private funds.
| [1] |
Navin NE. The first five years of single-cell cancer genomics and beyond[J]. Genome Res, 2015, 25(10): 1499−1507. doi: 10.1101/gr.191098.115
|
| [2] |
Human Genome Reference Program[EB/OL]. [2021-10-24]. https://www.genome.gov/Funded-Programs-Projects/Human-Genome-Reference-Program.
|
| [3] |
Human Genome Project Results[EB/OL]. (2018-11-12) [2021-10-24]. https://www.genome.gov/human-genome-project/results.
|
| [4] |
Genome Reference Consortium Human Build 38 patch release 13[EB/OL]. (2019-02-28) [2021-10-24]. https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/.
|
| [5] |
Genome Assemblies[EB/OL]. [2021-10-24]. https://www.ncbi.nlm.nih.gov/grc/data.
|
| [6] |
Human Genome Assembly GRCh38. p13[EB/OL]. [2021-10-24]. https://www.ncbi.nlm.nih.gov/grc/human/data.
|
| [7] |
Human (GRCh38. P13)[EB/OL]. [2021-10-24]. https://www.ensembl.org/Homo_sapiens/Info/Index.
|
| [8] |
Cell Biology by the Numbers[EB/OL]. [2021-10-24]. http://book.bionumbers.org/.
|
| [9] |
Shafin K, Pesout T, Lorig-Roach R, et al. Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes[J]. Nat Biotechnol, 2020, 38(9): 1044−1053. doi: 10.1038/s41587-020-0503-6
|
| [10] |
Baslan T, Kendall J, Rodgers L, et al. Genome-wide copy number analysis of single cells[J]. Nat Protoc, 2012, 7(6): 1024−1041. doi: 10.1038/nprot.2012.039
|
| [11] |
Bianconi E, Piovesan A, Facchin F, et al. An estimation of the number of cells in the human body[J]. Ann Hum Biol, 2013, 40(6): 463−471. doi: 10.3109/03014460.2013.807878
|
| [12] |
Sender R, Fuchs S, Milo R. Revised estimates for the number of human and bacteria cells in the body[J]. PLoS Biol, 2016, 14(8): e1002533. doi: 10.1371/journal.pbio.1002533
|
| [13] |
Vickaryous MK, Hall BK. Human cell type diversity, evolution, development, and classification with special reference to cells derived from the neural crest[J]. Biol Rev Camb Philos Soc, 2006, 81(3): 425−455. doi: 10.1017/S1464793106007068
|
| [14] |
Misevic G, Garbarino E. Glycan-to-glycan binding: molecular recognition through polyvalent interactions mediates specific cell adhesion[J]. Molecules, 2021, 26(2): 397. doi: 10.3390/molecules26020397
|
| [15] |
Svensson V, Beltrame EDV, Pachter L. A curated database reveals trends in single-cell transcriptomics[J]. Database(Oxford), 2020: baaa073.
|
| [16] |
Bagnoli JJWB, Ziegenhain C, Janjic A, et al. McSCRB-seq protocol: version 2[EB/OL]. (2018-05-22) [2021-10-24]. https://www.protocols.io/view/mcscrb-seq-protocol-p9kdr4w.
|
| [17] |
ScRNASeqDB[EB/OL]. [2021-10-24]. https://bioinfo.uth.edu/scrnaseqdb/.
|
| [18] |
Gene Expression Ominbus[EB/OL]. [2021-10-24]. https://www.ncbi.nlm.nih.gov/geo/.
|
| [19] |
Picelli S, Faridani OR, Björklund ÅK, et al. Full-length RNA-seq from single cells using Smart-seq2[J]. Nat Protoc, 2014, 9(1): 171−181. doi: 10.1038/nprot.2014.006
|
| [20] |
The Human Protein Atlas[EB/OL]. [2021-10-24]. https://www.proteinatlas.org/.
|
| [21] |
Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics Tissue-based map of the human proteome[J]. Science, 2015, 347(6220): 1260419. doi: 10.1126/science.1260419
|
| [22] |
Leung D, Jung I, Rajagopal N, et al. Integrative analysis of haplotype-resolved epigenomes across human tissues[J]. Nature, 2015, 518(7539): 350−354. doi: 10.1038/nature14217
|
| [23] |
Stevens WBC, Netea MG, Kater AP, et al. 'Trained immunity': consequences for lymphoid malignancies[J]. Haematologica, 2016, 101(12): 1460−1468. doi: 10.3324/haematol.2016.149252
|
| [24] |
Kawamura YI, Toyota M, Kawashima R, et al. DNA hypermethylation contributes to incomplete synthesis of carbohydrate determinants in gastrointestinal cancer[J]. Gastroenterology, 2008, 135(1): 142−151. doi: 10.1053/j.gastro.2008.03.031
|
| [25] |
Saeed S, Quintin J, Kerstens HHD, et al. Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity[J]. Science, 2014, 345(6204): 1251086. doi: 10.1126/science.1251086
|
| [26] |
Álvarez-Errico D, Vento-Tormo R, Sieweke M, et al. Epigenetic control of myeloid cell differentiation, identity and function[J]. Nat Rev Immunol, 2015, 15(1): 7−17. doi: 10.1038/nri3777
|
| [27] |
Stefan M, Wei C, Lombardi A, et al. Genetic-epigenetic dysregulation of thymic TSH receptor gene expression triggers thyroid autoimmunity[J]. Proc Natl Acad Sci, 2014, 111(34): 12562−12567. doi: 10.1073/pnas.1408821111
|
| [28] |
Zou Y, Sunshine MJ, Taniuchi I, et al. Epigenetic silencing of CD4 in T cells committed to the cytotoxic lineage[J]. Nat Genet, 2001, 29(3): 332−336. doi: 10.1038/ng750
|
| [29] |
Antony P, Rose M, Heidenreich A, et al. Epigenetic inactivation of ST6GAL1 in human bladder cancer[J]. BMC Cancer, 2014, 14: 901. doi: 10.1186/1471-2407-14-901
|
| [30] |
Lo PK, Zhou Q. Emerging techniques in single-cell epigenomics and their applications to cancer research[J]. J Clin Genom, 2018, 1(1). doi: 10.4172/JCG.1000103
|
| [31] |
Jerne NK, Köhler GJF, Milstein C. The Nobel Prize in physiology or medicine 1984[EB/OL] (2010-09-06) [2021-10-24]. https://www.nobelprize.org/prizes/medicine/1984/speedread/.
|
| [32] |
Doherty PC, Zinkernagel RM. The Nobel Prize in physiology or medicine 1996[EB/OL]. (2010-09-06) [2021-10-24].https://www.nobelprize.org/prizes/medicine/1996/speedread/.
|
| [33] |
Susumu T. The Nobel Prize in physiology or medicine 1987[EB/OL]. (2010-09-06) [2021-10-24]. https://www.nobelprize.org/prizes/medicine/1987/speedread/.
|
| [34] |
Gao R, Davis A, McDonald TO, et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer[J]. Nat Genet, 2016, 48(10): 1119−1130. doi: 10.1038/ng.3641
|
| [35] |
Taylor AM, Shih J, Ha G, et al. Genomic and functional approaches to understanding cancer aneuploidy[J]. Cancer Cell, 2018, 33(4): 676−689.e3. doi: 10.1016/j.ccell.2018.03.007
|
| [36] |
Davoli T, Uno H, Wooten EC, et al. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy[J]. Science, 2017, 355(6322): eaaf8399. doi: 10.1126/science.aaf8399
|
| [37] |
Wakeman JA, Hmadcha A, Soria B, et al. The immortal strand hypothesis: still non-randomly segregating opinions[J]. Biomol Concepts, 2012, 3(3): 203−211. doi: 10.1515/bmc-2011-0053
|
| [38] |
McVean GAT, Myers SR, Hunt S, et al. The fine-scale structure of recombination rate variation in the human genome[J]. Science, 2004, 304(5670): 581−584. doi: 10.1126/science.1092500
|
| [39] |
Coufal NG, Garcia-Perez JL, Peng GE, et al. L1 retrotransposition in human neural progenitor cells[J]. Nature, 2009, 460(7259): 1127−1131. doi: 10.1038/nature08248
|
| [40] |
Ando Y, Kwon ATJ, Shin JW. An era of single-cell genomics consortia[J]. Exp Mol Med, 2020, 52(9): 1409−1418. doi: 10.1038/s12276-020-0409-x
|
| [41] |
How big are genomes? [EB/OL]. [2021-10-24]. http://book.bionumbers.org/.
|
| [42] |
Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing[J]. Nature, 2011, 472(7341): 90−94. doi: 10.1038/nature09807
|
| [43] |
What is the macromolecular composition of the cell? [EB/OL]. [2021-10-24]. http://book.bionumbers.org/how-many-proteins-are-in-a-cell/.
|
| [44] |
Lynch M, Marinov GK. The bioenergetic costs of a gene[J]. Proc Natl Acad Sci, 2015, 112(51): 15690−15695. doi: 10.1073/pnas.1514974112
|
| [45] |
Gross A, Schoendube J, Zimmermann S, et al. Technologies for single-cell isolation[J]. Int J Mol Sci, 2015, 16(8): 16897−16919. doi: 10.3390/ijms160816897
|
| [46] |
Hu P, Zhang W, Xin H, et al. Single cell isolation and analysis[J]. Front Cell Dev Biol, 2016, 4: 116. doi: 10.3389/fcell.2016.00116
|
| [47] |
Zong C, Lu S, Chapman AR, et al. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell[J]. Science, 2012, 338(6114): 1622−1626. doi: 10.1126/science.1229164
|
| [48] |
Paolillo C, Londin E, Fortina P. Single-cell genomics[J]. Clin Chem, 2019, 65(8): 972−985. doi: 10.1373/clinchem.2017.283895
|
| [49] |
Wang Y, Waters J, Leung ML, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing[J]. Nature, 2014, 512(7513): 155−160. doi: 10.1038/nature13600
|
| [50] |
Grün D, Oudenaarden AV. Design and analysis of single-cell sequencing experiments[J]. Cell, 2015, 163(4): 799−810. doi: 10.1016/j.cell.2015.10.039
|
| [51] |
Zahn H, Steif A, Laks E, et al. Scalable whole-genome single-cell library preparation without preamplification[J]. Nat Methods, 2017, 14(2): 167−173. doi: 10.1038/nmeth.4140
|
| [52] |
Eirew P, Steif A, Khattra J, et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution[J]. Nature, 2015, 518(7539): 422−426. doi: 10.1038/nature13952
|
| [53] |
Kim C, Gao R, Sei E, et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing[J]. Cell, 2018, 173(4): 879−893.e13. doi: 10.1016/j.cell.2018.03.041
|
| [54] |
Nagasawa S, Kuze Y, Maeda I, et al. Genomic profiling reveals heterogeneous populations of ductal carcinoma in situ of the breast[J]. Commun Biol, 2021, 4(1): 438. doi: 10.1038/s42003-021-01959-9
|
| [55] |
Li Y, Xu X, Song L, et al. Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer[J]. Gigascience, 2012, 1(1): 12. doi: 10.1186/2047-217X-1-12
|
| [56] |
Xu X, Hou Y, Yin X, et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor[J]. Cell, 2012, 148(5): 886−895. doi: 10.1016/j.cell.2012.02.025
|
| [57] |
Tan KT, Kim HJ, Carrot-Zhang J, et al. Haplotype-resolved germline and somatic alterations in renal medullary carcinomas[J]. Genome Med, 2021, 13(1): 114. doi: 10.1186/s13073-021-00929-4
|
| [58] |
Duan M, Hao JF, Cui SJ, et al. Diverse modes of clonal evolution in HBV-related hepatocellular carcinoma revealed by single-cell genome sequencing[J]. Cell Res, 2018, 28(3): 359−373. doi: 10.1038/cr.2018.11
|
| [59] |
Hou Y, Song L, Zhu P, et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm[J]. Cell, 2012, 148(5): 873−885. doi: 10.1016/j.cell.2012.02.028
|
| [60] |
Jan M, Snyder TM, Corces-Zimmerman MR, et al. Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia[J]. Sci Transl Med, 2012, 4(149): 149ra118. doi: 10.1126/scitranslmed.3004315
|
| [61] |
Majeti R. Clonal evolution of pre-leukemic hematopoietic stem cells precedes human acute myeloid leukemia[J]. Best Pract Res Clin Haematol, 2014, 27(3–4): 229−234. doi: 10.1016/j.beha.2014.10.003
|
| [62] |
Morita K, Wang F, Jahn K, et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics[J]. Nat Commun, 2020, 11(1): 5327. doi: 10.1038/s41467-020-19119-8
|
| [63] |
Francis JM, Zhang CZ, Maire CL, et al. EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing[J]. Cancer Discov, 2014, 4(8): 956−971. doi: 10.1158/2159-8290.CD-13-0879
|
| [64] |
Gawad C, Koh W, Quake SR. Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics[J]. Proc Natl Acad Sci, 2014, 111(50): 17947−17952. doi: 10.1073/pnas.1420822111
|
| [65] |
Wang H, Meng D, Guo H, et al. Single-cell sequencing, an advanced technology in lung cancer research[J]. Onco Targets Ther, 2021, 14: 1895−1909. doi: 10.2147/OTT.S295102
|
| [66] |
Lohr JG, Stojanov P, Carter SL, et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy[J]. Cancer Cell, 2014, 25(1): 91−101. doi: 10.1016/j.ccr.2013.12.015
|
| [67] |
Lohr JG, Adalsteinsson VA, Cibulskis K, et al. Whole-exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer[J]. Nat Biotechnol, 2014, 32(5): 479−484. doi: 10.1038/nbt.2892
|
| [68] |
Heitzer E, Auer M, Gasch C, et al. Complex tumor genomes inferred from single circulating tumor cells by array-CGH and next-generation sequencing[J]. Cancer Res, 2013, 73(10): 2965−2975. doi: 10.1158/0008-5472.CAN-12-4140
|
| [69] |
Ni X, Zhuo M, Su Z, et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients[J]. Proc Natl Acad Sci, 2013, 110(52): 21083−21088. doi: 10.1073/pnas.1320659110
|
| [70] |
Satas G, Zaccaria S, Mon G, et al. Scarlet: single-cell tumor phylogeny inference with copy-number constrained mutation losses[J]. Cell Syst, 2020, 10(4): 323−332.e8. doi: 10.1016/j.cels.2020.04.001
|
| [71] |
Wang RJ, Lin DY, Jiang YC. Scope: a normalization and copy-number estimation method for single-cell DNA sequencing[J]. Cell Syst, 2020, 10(5): 445−452.e6. doi: 10.1016/j.cels.2020.03.005
|
| [72] |
Chen ZW, Gong FZ, Wan L, et al. RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data[J]. Bioinformatics, 2020, 36(11): 3299−3306. doi: 10.1093/bioinformatics/btaa172
|
| [73] |
Zaccaria S, Raphael BJ. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL[J]. Nat Biotechnol, 2021, 39(2): 207−214. doi: 10.1038/s41587-020-0661-6
|
| [74] |
Myers MA, Zaccaria S, Raphael BJ. Identifying tumor clones in sparse single-cell mutation data[J]. Bioinformatics, 2020, 36: i186−i193. doi: 10.1093/bioinformatics/btaa449
|
| [75] |
Ferronika P, Bos HVD, Taudt A, et al. Copy number alterations assessed at the single-cell level revealed mono- and polyclonal seeding patterns of distant metastasis in a small-cell lung cancer patient[J]. Ann Oncol, 2017, 28(7): 1668−1670. doi: 10.1093/annonc/mdx182
|
| [76] |
Kaur P, Campo D, Porras TB, et al. A pilot study for the feasibility of exome-sequencing in circulating tumor cells versus single metastatic biopsies in breast cancer[J]. Int J Mol Sci, 2020, 21(14): 1−29. doi: 10.3390/ijms21144826
|
| [77] |
Keller L, Pantel K. Unravelling tumour heterogeneity by single-cell profiling of circulating tumour cells[J]. Nat Rev Cancer, 2019, 19(10): 553−567. doi: 10.1038/s41568-019-0180-2
|
| [78] |
Liu RY, Gao QS, Foltz SM, et al. Co-evolution of tumor and immune cells during progression of multiple myeloma[J]. Nat Commun, 2021, 12(1): 2559. doi: 10.1038/s41467-021-22804-x
|
| [79] |
Wu CY, Lau BT, Kim HS, et al. Integrative single-cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer[J]. Nat Biotechnol, 2021, 39(10): 1259−1269. doi: 10.1038/s41587-021-00911-w
|
| [80] |
Nieto P, Elosua-Bayes M, Trincado JL, et al. A single-cell tumor immune atlas for precision oncology[J]. Genome Res, 2021, 31(10): 1913−1926. doi: 10.1101/gr.273300.120
|
| [81] |
Cai X, Evrony GD, Lehmann HS, et al. Single-cell, genome-wide sequencing identifies clonal somatic copy-number variation in the human brain[J]. Cell Rep, 2015, 10(4): 645. doi: 10.1016/j.celrep.2015.01.028
|
| [82] |
McConnell MJ, Lindberg MR, Brennand KJ, et al. Mosaic copy number variation in human neurons[J]. Science, 2013, 342(6158): 632−637. doi: 10.1126/science.1243472
|
| [83] |
Rehen SK, McConnell MJ, Kaushal D, et al. Chromosomal variation in neurons of the developing and adult mammalian nervous system[J]. Proc Natl Acad Sci, 2001, 98(23): 13361−13366. doi: 10.1073/pnas.231487398
|
| [84] |
Knouse KA, Wu J, Whittaker CA, et al. Single cell sequencing reveals low levels of aneuploidy across mammalian tissues[J]. Proc Natl Acad Sci, 2014, 111(37): 13409−13414. doi: 10.1073/pnas.1415287111
|
| [85] |
Wang J, Fan HC, Behr B, et al. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm[J]. Cell, 2012, 150(2): 402−412. doi: 10.1016/j.cell.2012.06.030
|
| [1] | Yanhong Zhang, Li Li, Shangen Zheng, Yingkai Xu, Zihan Yuan, Chengyu Liu, Yu Zhang, Yao Zheng, Bicheng Hu, Wanbing Liu, Lei Liu. Comparison of Blood Coagulation Function of Severe Patients Infected with SARS-CoV-2 Original Strain and Omicron Variant in China: A Single Center Retrospective Analysis[J]. Blood&Genomics, 2024, 8(2): 10006. DOI: 10.70322/BG20240210006 |
| [2] | Han Yao, Ting Chen, Ruihao Huang, Xiaoqi Wang, Xiaojuan Deng, Jun Rao, Xixi Xiang, Yuqing Liu, Lu Zhao, Jia Liu, Lidan Zhu, Jiali Li, Yimei Feng, Lei Gao, Peiyan Kong, Xi Zhang. The Outcomes and Risk Factors for Late-Onset Haemorrhagic Cystitis after Allogeneic Haematopoietic Transplantation for Acute Leukaemia: A Retrospective, Single-Centre Observational Study[J]. Blood&Genomics, 2024, 8(2): 10005. DOI: 10.70322/BG20240210005 |
| [3] | 2021 Editorial Working Conference of Blood and Genomics Held in Chongqing[J]. Blood&Genomics, 2021, 5(1): 72-72. |
| [4] | Fumou Sun, Yan Cheng, Siegfried Janz. Genomic instability in Multiple Myeloma-relevance for Clinical Outcome and Efficacy of Therapy[J]. Blood&Genomics, 2019, 3(2): 89-99. DOI: 10.46701/APJBG.2019022019126 |
| [5] | Jingjing Gao, Xiongpeng Zhu, Jinzhe Tan, Mingquan Wang, Wenqian Xu, Jhy Sheng Chang. Pedigree analysis for the rare RHD DVa allele: serological and molecular studies[J]. Blood&Genomics, 2019, 3(1): 47-50. DOI: 10.46701/APJBG.2019012019102 |
| [6] | Xiaofei Li, YuShiang Lin, Tingting Liu, Ye Zhang, Guangyan Zhuang, Tianhong Miao, Zhiyuan Xu. Serological and molecular analysis of ABO and Rh blood group chimeras[J]. Blood&Genomics, 2018, 2(4): 263-268. DOI: 10.46701/APJBG.2018042018137 |
| [7] | Guadalupe E. Estrada-Chávez, Roberto Estrada, Guadalupe Chavez, María-Elisa Vega Memije, Roberto Arenas Guzmán, Maricela García-Lechuga, Julio Granados, Lucia Rangel-Gamboa. HLA class Ⅱ alleles in human sporotrichosis in Mexican Amerindians[J]. Blood&Genomics, 2018, 2(3): 183-190. DOI: 10.46701/APJBG.2018032018119 |
| [8] | Zhiyuan Xu, Haochun Chang, Xiaojie Zhu, Chen Cao, Tianhong Miao, Jiwu Gong, Yu-Shiang Lin. Serological and molecular analysis of anti-Tja: Case report[J]. Blood&Genomics, 2018, 2(2): 125-128. DOI: 10.46701/APJBG.2018022018108 |
| [9] | Yanjun Jia, Wei Li, Lijun Wang, Na Liu, Dongmei Wang, Dongmei Li, Yuanyuan Jing, Jie Wang, Zhongmei Wang, Xiaoyan Shan. Allele and haplotype frequencies of human leukocyte antigen-A, -B, -C,-DRB1, and -DQB1 in Chinese patients with hematological diseases[J]. Blood&Genomics, 2018, 2(1): 27-37. DOI: 10.46701/APJBG.2018012017064 |
| [10] | Pengfei Shi, Yaping Xie, Ying Xu, Xilian Huang, Kuang Chen, Shenxian Qian. Active EB virus infection in adults: a case series of 8 patients in a single institution and a review of related literature[J]. Blood&Genomics, 2017, 1(3): 53-58. DOI: 10.46701/APJBG.2017032017034 |
| 1. | Yuqing Wang, Xi Zhang, Zheng Wang. Cellular barcoding: From developmental tracing to anti-tumor drug discovery. Cancer Letters, 2023. DOI:10.1016/j.canlet.2023.216281 |
Figures(4)
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad:
