Simultaneous genomic identification and profiling of a single cell using semiconductor-based next generation sequencing.
ABSTRACT: Combining single-cell methods and next-generation sequencing should provide a powerful means to understand single-cell biology and obviate the effects of sample heterogeneity. Here we report a single-cell identification method and seamless cancer gene profiling using semiconductor-based massively parallel sequencing. A549 cells (adenocarcinomic human alveolar basal epithelial cell line) were used as a model. Single-cell capture was performed using laser capture microdissection (LCM) with an Arcturus® XT system, and a captured single cell and a bulk population of A549 cells (? 10(6) cells) were subjected to whole genome amplification (WGA). For cell identification, a multiplex PCR method (AmpliSeq™ SNP HID panel) was used to enrich 136 highly discriminatory SNPs with a genotype concordance probability of 10(31-35). For cancer gene profiling, we used mutation profiling that was performed in parallel using a hotspot panel for 50 cancer-related genes. Sequencing was performed using a semiconductor-based bench top sequencer. The distribution of sequence reads for both HID and Cancer panel amplicons was consistent across these samples. For the bulk population of cells, the percentages of sequence covered at coverage of more than 100 × were 99.04% for the HID panel and 98.83% for the Cancer panel, while for the single cell percentages of sequence covered at coverage of more than 100 × were 55.93% for the HID panel and 65.96% for the Cancer panel. Partial amplification failure or randomly distributed non-amplified regions across samples from single cells during the WGA procedures or random allele drop out probably caused these differences. However, comparative analyses showed that this method successfully discriminated a single A549 cancer cell from a bulk population of A549 cells. Thus, our approach provides a powerful means to overcome tumor sample heterogeneity when searching for somatic mutations.
Project description:Single Gene Disorders (SGD) are still routinely diagnosed using PCR-based assays that need to be developed and validated for each individual disease-specific gene fragment. The TruSight One sequencing panel currently covers 12 Mb of genomic content, including 4813 genes associated with a clinical phenotype. When only a limited number of cells are available, whole genome amplification (WGA) is required prior to DNA target capture techniques such as the TruSight One panel. In this study, we compared 4 different WGA methods in combination with the TruSight One sequencing panel to perform single nucleotide polymorphism (SNP) genotyping starting from 3 micro-manipulated cells. This setting simulates clinical settings such as day-5 blastocyst biopsy for Preimplantation Genetic Testing (PGT), liquid biopsy of circulating tumor cells (CTCs) and cancer-cell profiling. Bulk cell samples were processed alongside these WGA samples to serve as a performance reference. Target coverage, coverage uniformity and SNP calling accuracy obtained using any of the WGA, is inferior to the results obtained on bulk cell samples. However, results after REPLI-g come close. Compared to the other WGA methods, the method using REPLI-g WGA results in a better coverage of the targeted genomic regions with a more uniform read depth. Consequently, this method also results in a more accurate SNP calling and could be considered for clinical genotyping of a limited number of cells.
Project description:BACKGROUND:Recent advances in semiconductor sequencing platform (SSP) have provided new methods for preimplantation genetic diagnosis/screening (PGD/S). The present study aimed to evaluate the applicability and efficiency of SSP in PGD/S. METHODS:The artificial positive single-cell-like DNAs and normal single-cell samples were chosen to test our semiconductor sequencing platform for preimplantation genetic diagnosis/screening (SSP-PGD/S) method with two widely used whole-genome amplification (WGA) kits. A total of 557 single blastomeres were collected from in vitro fertilization (IVF) couples, and their WGA products were processed and analyzed by our SSP-PGD/S method in comparison with array comparative genomic hybridization (array-CGH). RESULTS:Our SSP-PGD/S method indicated high compatibilities with two commercial WGA kits. For 557 single blastomeres, our method with four million reads in average could detect 24-chromosome aneuploidies as well as microdeletion/microduplication of the size over 4?Mb, providing 100% consistent conclusion with array-CGH method in the classification of whether it was transplantable. CONCLUSIONS:Our studies suggested that SSP-PGD/S represents a valuable alternative to array-CGH and brought PGD/S into a new era of more rapid, accurate, and economic.
Project description:Somatic mosaicism occurs throughout normal development and contributes to numerous disease etiologies, including tumorigenesis and neurological disorders. Intratumor genetic heterogeneity is inherent to many cancers, creating challenges for effective treatments. Unfortunately, analysis of bulk DNA masks subclonal phylogenetic architectures created by the acquisition and distribution of somatic mutations amongst cells. As a result, single-cell genetic analysis is becoming recognized as vital for accurately characterizing cancers. Despite this, methods for single-cell genetics are lacking. Here we present an automated microfluidic workflow enabling efficient cell capture, lysis, and whole genome amplification (WGA). We find that ~90% of the genome is accessible in single cells with improved uniformity relative to current single-cell WGA methods. Allelic dropout (ADO) rates were limited to 13.75% and variant false discovery rates (SNV FDR) were 4.11x10(-6), on average. Application to ER-/PR-/HER2+ breast cancer cells and matched normal controls identified novel mutations that arose in a subpopulation of cells and effectively resolved the segregation of known cancer-related mutations with single-cell resolution. Finally, we demonstrate effective cell classification using mutation profiles with 10X average exome coverage depth per cell. Our data demonstrate an efficient automated microfluidic platform for single-cell WGA that enables the resolution of somatic mutation patterns in single cells.
Project description:Intratumoral genetic heterogeneity may impact disease outcome. Gold standard for dissecting clonal heterogeneity are single-cell analyses. Here, we present an efficient workflow based on an advanced Single-Cell Printer (SCP) device for the study of gene variants in single cancer cells. To allow for precise cell deposition into microwells the SCP was equipped with an automatic dispenser offset compensation, and the 384-microwell plates were electrostatically neutralized. The ejection efficiency was 99.7% for fluorescent beads (n = 2304) and 98.7% for human cells (U-2 OS or Kasumi-1 cancer cell line, acute myeloid leukemia [AML] patient; n = 150). Per fluorescence microscopy, 98.8% of beads were correctly delivered into the wells. A subset of single cells (n = 81) was subjected to whole genome amplification (WGA), which was successful in all cells. On empty droplets, a PCR on LINE1 retrotransposons yielded no product after WGA, verifying the absence of free-floating DNA in SCP-generated droplets. Representative gene variants identified in bulk specimens were sequenced in single-cell WGA DNA. In U-2 OS, 22 of 25 cells yielded results for both an SLC34A2 and TET2 mutation site, including cells harboring the SLC34A2 but not the TET2 mutation. In one cell, the TET2 mutation analysis was inconclusive due to allelic dropout, as assessed via polymorphisms located close to the mutation. Of Kasumi-1, 23 of 33 cells with data on both the KIT and TP53 mutation site harbored both mutations. In the AML patient, 21 of 23 cells were informative for a TP53 polymorphism; the identified alleles matched the loss of chromosome arm 17p. The advanced SCP allows efficient, precise and gentle isolation of individual cells for subsequent WGA and routine PCR/sequencing-based analyses of gene variants. This makes single-cell information readily accessible to a wide range of applications and can provide insights into clonal heterogeneity that were indeterminable solely by analyses of bulk specimens.
Project description:<h4>Background</h4>Whole genome amplification (WGA) is currently a prerequisite for single cell whole genome or exome sequencing. Depending on the method used the rate of artifact formation, allelic dropout and sequence coverage over the genome may differ significantly.<h4>Results</h4>The largest difference between the evaluated protocols was observed when analyzing the target coverage and read depth distribution. These differences also had impact on the downstream variant calling. Conclusively, the products from the AMPLI1 and MALBAC kits were shown to be most similar to the bulk samples and are therefore recommended for WGA of single cells.<h4>Discussion</h4>In this study four commercial kits for WGA (AMPLI1, MALBAC, Repli-G and PicoPlex) were used to amplify human single cells. The WGA products were exome sequenced together with non-amplified bulk samples from the same source. The resulting data was evaluated in terms of genomic coverage, allelic dropout and SNP calling.
Project description:Single-cell sequencing is emerging as an important tool for studies of genomic heterogeneity. Whole genome amplification (WGA) is a key step in single-cell sequencing workflows and a multitude of methods have been introduced. Here, we compare three state-of-the-art methods on both bulk and single-cell samples of E. coli DNA: Multiple Displacement Amplification (MDA), Multiple Annealing and Looping Based Amplification Cycles (MALBAC), and the PicoPLEX single-cell WGA kit (NEB-WGA). We considered the effects of reaction gain on coverage uniformity, error rates and the level of background contamination. We compared the suitability of the different WGA methods for the detection of copy-number variations, for the detection of single-nucleotide polymorphisms and for de-novo genome assembly. No single method performed best across all criteria and significant differences in characteristics were observed; the choice of which amplifier to use will depend strongly on the details of the type of question being asked in any given experiment.
Project description:The nature and pace of genome mutation is largely unknown. Because standard methods sequence DNA from populations of cells, the genetic composition of individual cells is lost, de novo mutations in cells are concealed within the bulk signal and per cell cycle mutation rates and mechanisms remain elusive. Although single-cell genome analyses could resolve these problems, such analyses are error-prone because of whole-genome amplification (WGA) artefacts and are limited in the types of DNA mutation that can be discerned. We developed methods for paired-end sequence analysis of single-cell WGA products that enable (i) detecting multiple classes of DNA mutation, (ii) distinguishing DNA copy number changes from allelic WGA-amplification artefacts by the discovery of matching aberrantly mapping read pairs among the surfeit of paired-end WGA and mapping artefacts and (iii) delineating the break points and architecture of structural variants. By applying the methods, we capture DNA copy number changes acquired over one cell cycle in breast cancer cells and in blastomeres derived from a human zygote after in vitro fertilization. Furthermore, we were able to discover and fine-map a heritable inter-chromosomal rearrangement t(1;16)(p36;p12) by sequencing a single blastomere. The methods will expedite applications in basic genome research and provide a stepping stone to novel approaches for clinical genetic diagnosis.
Project description:Establishment and subsequent validation of a aCGH protocol for WGA (whole genome amplification) products originating from single cell or low amount of starting material (i.e. microdissected FFPE tissue samples). The establishment of the protocol involved testing of three DNA labeling protocols. Two labeling protocols were designed specifically for Ampli1(TM) WGA products. Additionally random primed isothermal (Klenow-based) labeling approach was tested (Möhlendick et al., PLoS One. 2013 Jun 25;8(6):e67031.). In addition two different types of reference samples were tested and reference based on single-cell WGA products was chosen as most suitable in the end. The validation of the protocol assessed the following aspects: (1) performance of the protocol on primary and reamplified WGA products, (2) accuracy of the protocol in term of sensitivity of the CNA detection, (3) accuracy in terms of recapitulation of complex patterns of CNAs, (4) accuracy in terms of quantitative assessment of the CNAs, (5) ability to detect genomic heterogeneity of single cells (obtained either from in vitro cultures or from clinical patient material), (6) ability to detect minimal regions of aberration within a panel of disseminated cancer cells and corresponding tumor tissues. Establishment and validation of the single-cell aCGH protocol: two condition experiment (i.e. PCR-based labeling technique 1 vs. PCR-based labeling technique 2; PCR-based labeling technique 2 vs. random-primed isothermal (Klenow) labeling approach; reference DNA from cell pool WGA product vs. reference DNA from single-cell WGA products). Validation of the protocol: comparison of the CNA profiles between single-cell WGA products and corresponding bulk DNA. Analysis of the DCC and corresponding FFPE tumor tissue samples: single-condition experiment performed on samples collected at different stages of the disease (DCCs from bone marrow) and/or from different sites (primary tumor-breast; metastasis-lymph nodes; DCCs-bone marrow). Microarray data is corresponding data depicted in the paper manuscript titled: Reliable single cell array CGH for clinical samples
Project description:Mitochondrial metabolism plays an essential role in various biological processes of cancer cells. Herein, we established an experimental procedure for the metabolic assessment of mitochondria in cancer cells. We examined procedures for mitochondrial isolation coupled with various mitochondrial extraction buffers in three major cancer cell lines (PANC1, A549, and MDA-MB-231) and identified a potentially optimal and generalized approach. The purity of the mitochondrial fraction isolated by the selected protocol was verified using specific protein markers of cellular components, and the ultrastructure of the isolated mitochondria was also analyzed by transmission electron microscopy. The isolation procedure, involving a bead beater for cell lysis, a modified sucrose buffer, and differential centrifugation, appeared to be a suitable method for the extraction of mitochondria from cancer cells. Electron micrographs indicated an intact two-layer membrane and inner structures of mitochondria isolated by this procedure. Metabolomic and lipidomic analyses were conducted to examine the metabolic phenotypes of the mitochondria-enriched fractions and associated bulk cancer cells. A total of 44 metabolites, including malate and succinate, occurred at significantly higher levels in the mitochondrial fractions, whereas 51 metabolites, including citrate, oxaloacetate, and fumarate of the Krebs cycle and the oncometabolites glutamine and glutamate, were reduced in mitochondria compared to that in the corresponding bulk cells of PANC1. Similar patterns were observed in mitochondria and bulk cells of MDA-MB-231 and A549 cell lines. A clear difference between the lipid profiles of bulk PANC1, MDA-MB-231, and A549 and corresponding mitochondrial fractions of these cell lines was detected by principal component analysis. In conclusion, we developed an experimental procedure for a large-scale metabolic assessment for suborganelle metabolic profiling and multiple omics data integration in cancer cells with broad applications.
Project description:To allow multiple genetic analyses on a single cell, whole genome amplification (WGA) is required. Unfortunately, studies comparing different WGA methods for downstream human identification Short Tandem Repeat (STR) analysis remain absent. Therefore, the aim of this work was to assess the performance of four commercially available WGA kits for downstream human identification STR profiling on a B-lymphoblastoid cell line. The performance was assessed using an input of one or three micromanipulated cells. REPLI-g showed a very low dropout rate, as it was the only WGA method in this study that could provide a complete STR profile in some of its samples. Although Ampli1, DOPlify and PicoPLEX did not detect all selected STR markers, they seem suitable for genetic identification in single-cell applications.