Genome-wide analysis of single nucleotide variants allows for robust and accurate assessment of clonal derivation in cell lines used to produce biologics

A clonally derived (or “monoclonal”) cell line is a cell population derived from a single progenitor cell. Clonally derived cell lines are required for many biotechnological applications. For instance, recombinant mammalian cells used to produce therapeutic proteins are expected by regulatory authorities to be clonally derived. Assurance of clonal derivation (or “clonality”) is usually obtained from the characterization of the procedure used for cell cloning, for instance by assessing the success rate of single-cell sorting but not by assessing the cell line itself. We have developed a method to assess clonal derivation directly from the genetic makeup of cells. The genomic test of clonality is based on whole-genome sequencing and statistical analysis of single nucleotide variants. This approach quantifies the clonal fractions present in nonclonal samples and it provides a measure of the probability that a cell line is derived from a single cell. Upon experimental validation of the test, we show that it is highly accurate and that it can robustly detect minor clonal fractions of as little as 1% of the cell population. Moreover, we find that it is applicable to various cell line development protocols. This approach can simplify development protocols and shorten timelines while ensuring clonal derivation with high confidence.     

Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy

Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.     

Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform

Rapid advances in sequencing technology have changed the experimental landscape of microbial ecology. In the last 10 years, the field has moved from sequencing hundreds of 16S rRNA gene fragments per study using clone libraries to the sequencing of millions of fragments per study using next-generation sequencing technologies from 454 and Illumina. As these technologies advance, it is critical to assess the strengths, weaknesses, and overall suitability of these platforms for the interrogation of microbial communities. Here, we present an improved method for sequencing variable regions within the 16S rRNA gene using Illumina''s MiSeq platform, which is currently capable of producing paired 250-nucleotide reads. We evaluated three overlapping regions of the 16S rRNA gene that vary in length (i.e., V34, V4, and V45) by resequencing a mock community and natural samples from human feces, mouse feces, and soil. By titrating the concentration of 16S rRNA gene amplicons applied to the flow cell and using a quality score-based approach to correct discrepancies between reads used to construct contigs, we were able to reduce error rates by as much as two orders of magnitude. Finally, we reprocessed samples from a previous study to demonstrate that large numbers of samples could be multiplexed and sequenced in parallel with shotgun metagenomes. These analyses demonstrate that our approach can provide data that are at least as good as that generated by the 454 platform while providing considerably higher sequencing coverage for a fraction of the cost.     

Haplotype-Phased Synthetic Long Reads from Short-Read Sequencing

Next-generation DNA sequencing has revolutionized the study of biology. However, the short read lengths of the dominant instruments complicate assembly of complex genomes and haplotype phasing of mixtures of similar sequences. Here we demonstrate a method to reconstruct the sequences of individual nucleic acid molecules up to 11.6 kilobases in length from short (150-bp) reads. We show that our method can construct 99.97%-accurate synthetic reads from bacterial, plant, and animal genomic samples, full-length mRNA sequences from human cancer cell lines, and individual HIV env gene variants from a mixture. The preparation of multiple samples can be multiplexed into a single tube, further reducing effort and cost relative to competing approaches. Our approach generates sequencing libraries in three days from less than one microgram of DNA in a single-tube format without custom equipment or specialized expertise.     

Rapid Fine Conformational Epitope Mapping Using Comprehensive Mutagenesis and Deep Sequencing

Knowledge of the fine location of neutralizing and non-neutralizing epitopes on human pathogens affords a better understanding of the structural basis of antibody efficacy, which will expedite rational design of vaccines, prophylactics, and therapeutics. However, full utilization of the wealth of information from single cell techniques and antibody repertoire sequencing awaits the development of a high throughput, inexpensive method to map the conformational epitopes for antibody-antigen interactions. Here we show such an approach that combines comprehensive mutagenesis, cell surface display, and DNA deep sequencing. We develop analytical equations to identify epitope positions and show the method effectiveness by mapping the fine epitope for different antibodies targeting TNF, pertussis toxin, and the cancer target TROP2. In all three cases, the experimentally determined conformational epitope was consistent with previous experimental datasets, confirming the reliability of the experimental pipeline. Once the comprehensive library is generated, fine conformational epitope maps can be prepared at a rate of four per day.     

One Bacterial Cell,One Complete Genome

While the bulk of the finished microbial genomes sequenced to date are derived from cultured bacterial and archaeal representatives, the vast majority of microorganisms elude current culturing attempts, severely limiting the ability to recover complete or even partial genomes from these environmental species. Single cell genomics is a novel culture-independent approach, which enables access to the genetic material of an individual cell. No single cell genome has to our knowledge been closed and finished to date. Here we report the completed genome from an uncultured single cell of Candidatus Sulcia muelleri DMIN. Digital PCR on single symbiont cells isolated from the bacteriome of the green sharpshooter Draeculacephala minerva bacteriome allowed us to assess that this bacteria is polyploid with genome copies ranging from approximately 200–900 per cell, making it a most suitable target for single cell finishing efforts. For single cell shotgun sequencing, an individual Sulcia cell was isolated and whole genome amplified by multiple displacement amplification (MDA). Sanger-based finishing methods allowed us to close the genome. To verify the correctness of our single cell genome and exclude MDA-derived artifacts, we independently shotgun sequenced and assembled the Sulcia genome from pooled bacteriomes using a metagenomic approach, yielding a nearly identical genome. Four variations we detected appear to be genuine biological differences between the two samples. Comparison of the single cell genome with bacteriome metagenomic sequence data detected two single nucleotide polymorphisms (SNPs), indicating extremely low genetic diversity within a Sulcia population. This study demonstrates the power of single cell genomics to generate a complete, high quality, non-composite reference genome within an environmental sample, which can be used for population genetic analyzes.     

Prenatal detection of aneuploidy and imbalanced chromosomal arrangements by massively parallel sequencing

Fetal chromosomal abnormalities are the most common reasons for invasive prenatal testing. Currently, G-band karyotyping and several molecular genetic methods have been established for diagnosis of chromosomal abnormalities. Although these testing methods are highly reliable, the major limitation remains restricted resolutions or can only achieve limited coverage on the human genome at one time. The massively parallel sequencing (MPS) technologies which can reach single base pair resolution allows detection of genome-wide intragenic deletions and duplication challenging karyotyping and microarrays as the tool for prenatal diagnosis. Here we reported a novel and robust MPS-based method to detect aneuploidy and imbalanced chromosomal arrangements in amniotic fluid (AF) samples. We sequenced 62 AF samples on Illumina GAIIx platform and with averagely 0.01× whole genome sequencing data we detected 13 samples with numerical chromosomal abnormalities by z-test. With up to 2× whole genome sequencing data we were able to detect microdeletion/microduplication (ranged from 1.4 Mb to 37.3 Mb of 5 samples from chorionic villus sampling (CVS) using SeqSeq algorithm. Our work demonstrated MPS is a robust and accurate approach to detect aneuploidy and imbalanced chromosomal arrangements in prenatal samples.     

FAST-SeqS: a simple and efficient method for the detection of aneuploidy by massively parallel sequencing

Massively parallel sequencing of cell-free, maternal plasma DNA was recently demonstrated to be a safe and effective screening method for fetal chromosomal aneuploidies. Here, we report an improved sequencing method achieving significantly increased throughput and decreased cost by replacing laborious sequencing library preparation steps with PCR employing a single primer pair designed to amplify a discrete subset of repeated regions. Using this approach, samples containing as little as 4% trisomy 21 DNA could be readily distinguished from euploid samples.     

A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing

Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4∼9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.     

Don't put all your eggs in one basket: a cost‐effective and powerful method to optimize primer choice for rRNA environmental community analyses using the Fluidigm Access Array

With the increasing democratization of high‐throughput sequencing (HTS) technologies, along with the concomitant increase in sequence yield per dollar, many researchers are exploring HTS for microbial community ecology. Many elements of experimental design can drastically affect the final observed community structure, notably the choice of primers for amplification prior to sequencing. Some targeted microbes can fail to amplify due to primer‐targeted sequence divergence and be omitted from obtained sequences, leading to differences among primer pairs in the sequenced organisms even when targeting the same community. This potential source of taxonomic bias in HTS makes it prudent to investigate how primer choice will affect the sequenced community prior to investing in a costly community‐wide sequencing effort. Here, we use Fluidigm's microfluidic Access Arrays (IFC) followed by Illumina^® MiSeq Nano sequencing on a culture‐derived local mock community to demonstrate how this approach allows for a low‐cost combinatorial investigation of primer pairs and experimental samples (up to 48 primer pairs and 48 samples) to determine the most effective primers that maximize obtained communities whilst minimizing taxonomic biases.     

Rethinking microbial diversity analysis in the high throughput sequencing era

The analysis of amplified and sequenced 16S rRNA genes has become the most important single approach for microbial diversity studies. The new sequencing technologies allow for sequencing thousands of reads in a single run and a cost-effective option is split into a single run across many samples. However for this type of investigation the key question that needs to be answered is how many samples can be sequenced without biasing the results due to lack of sequence representativeness? In this work we demonstrated that the level of sequencing effort used for analyzing soil microbial communities biases the results and determines the most effective type of analysis for small and large datasets. Many simulations were performed with four independent pyrosequencing-generated 16S rRNA gene libraries from different environments. The analysis performed here illustrates the lack of resolution of OTU-based approaches for datasets with low sequence coverage. This analysis should be performed with at least 90% of sequence coverage. Diversity index values increase with sample size making normalization of the number of sequences in all samples crucial. An important finding of this study was the advantage of phylogenetic approaches for examining microbial communities with low sequence coverage. However, if the environments being compared were closely related, a deeper sequencing would be necessary to detect the variation in the microbial composition.     

Scaling up DNA metabarcoding for freshwater macrozoobenthos monitoring

相似文献   

Multiple reaction monitoring to identify sites of protein phosphorylation with high sensitivity

Phosphorylation governs the activity of many proteins. Insight into molecular mechanisms in biology would be immensely improved by robust, sensitive methods for identifying precisely sites of phosphate addition. An approach to selective mapping of protein phosphorylation sites on a specific target protein of interest using LC-MS is described here. In this approach multiple reaction monitoring is used as an extremely sensitive MS survey scan for potential phosphopeptides from a known protein. This is automatically followed by peptide sequencing and subsequent location of the phosphorylation site; both of these steps occur in a single LC-MS run, providing greater efficiency of sample use. The method is capable of detecting and sequencing phosphopeptides at low femtomole levels with high selectivity. As proof of the value of this approach in an experimental setting, a key Schizosaccharomyces pombe cell cycle regulatory protein, Cyclin B, was purified, and associated proteins were identified. Phosphorylation sites on these proteins were located. The technique, which we have called multiple reaction monitoring-initiated detection and sequencing (MIDAS), is shown to be a highly sensitive approach to the determination of protein phosphorylation.     

Statistical guidance for experimental design and data analysis of mutation detection in rare monogenic mendelian diseases by exome sequencing

Recently, whole-genome sequencing, especially exome sequencing, has successfully led to the identification of causal mutations for rare monogenic Mendelian diseases. However, it is unclear whether this approach can be generalized and effectively applied to other Mendelian diseases with high locus heterogeneity. Moreover, the current exome sequencing approach has limitations such as false positive and false negative rates of mutation detection due to sequencing errors and other artifacts, but the impact of these limitations on experimental design has not been systematically analyzed. To address these questions, we present a statistical modeling framework to calculate the power, the probability of identifying truly disease-causing genes, under various inheritance models and experimental conditions, providing guidance for both proper experimental design and data analysis. Based on our model, we found that the exome sequencing approach is well-powered for mutation detection in recessive, but not dominant, Mendelian diseases with high locus heterogeneity. A disease gene responsible for as low as 5% of the disease population can be readily identified by sequencing just 200 unrelated patients. Based on these results, for identifying rare Mendelian disease genes, we propose that a viable approach is to combine, sequence, and analyze patients with the same disease together, leveraging the statistical framework presented in this work.     

A computational index derived from whole-genome copy number analysis is a novel tool for prognosis in early stage lung squamous cell carcinoma

Squamous cell carcinoma of the lung is remarkable for the extent to which the same chromosomal abnormalities are detected in individual tumours. We have used next generation sequencing at low coverage to produce high resolution copy number karyograms of a series of 89 non-small cell lung tumours specifically of the squamous cell subtype. Because this methodology is able to create karyograms from formalin-fixed paraffin-embedded material, we were able to use archival stored samples for which survival data were available and correlate frequently occurring copy number changes with disease outcome. No single region of genomic change showed significant correlation with survival. However, adopting a whole-genome approach, we devised an algorithm that relates to total genomic damage, specifically the relative ratios of copy number states across the genome. This algorithm generated a novel index, which is an independent prognostic indicator in early stage squamous cell carcinoma of the lung.     

Cytopathological evaluations combined RNA and protein analyses on defined cell regions using single frozen tissue block

The co-existence of multiple cell components in tissue samples is the main obstacle for precise molecular evaluation on defined cell types. Based on morphological examination, we developed an efficient approach for paralleled RNA and protein isolations from an identical histological region in frozen tissue section. The RNA and protein samples prepared were sufficient for RT-PCR and Western blot analyses, and the results obtained were well coincident each other as well as with the corresponding parameters revealed from immunohistochemical examinations. By this way, the sampling problem caused by cell-cross contamination can be largely avoided, committing the experimental data more specific to a denned cell type. These novel methods thus allow us to use single tissue block for a comprehensive study by integration of conventional cytological evaluations with nucleic acid and protein analyses.     

A method for imaging single molecules at the plasma membrane of live cells within tissue slices

Recent advances in light microscopy allow individual biological macromolecules to be visualized in the plasma membrane and cytosol of live cells with nanometer precision and ∼10-ms time resolution. This allows new discoveries to be made because the location and kinetics of molecular interactions can be directly observed in situ without the inherent averaging of bulk measurements. To date, the majority of single-molecule imaging studies have been performed in either unicellular organisms or cultured, and often chemically fixed, mammalian cell lines. However, primary cell cultures and cell lines derived from multi-cellular organisms might exhibit different properties from cells in their native tissue environment, in particular regarding the structure and organization of the plasma membrane. Here, we describe a simple approach to image, localize, and track single fluorescently tagged membrane proteins in freshly prepared live tissue slices and demonstrate how this method can give information about the movement and localization of a G protein–coupled receptor in cardiac tissue slices. In principle, this experimental approach can be used to image the dynamics of single molecules at the plasma membrane of many different soft tissue samples and may be combined with other experimental techniques.