Analysis of fragment size distribution of cell-free DNA: A potential non-invasive marker to monitor graft damage in living-related liver transplantation for inborn errors of metabolism.

Graft-derived-cell-free DNA (Gcf-DNA) in plasma is a promising biomarker to monitor graft-rejection after liver transplantation (LTx). However, current methods of measuring Gcf-DNA have several limitations including high cost, long turnaround-time and the need to request donor's genetic information. In this study, eleven patients diagnosed with different inborn errors of metabolism (IEMs) who required living-related LTx were enrolled in order to establish a potentially useful noninvasive method to monitor graft damage. Circulating cell-free DNA (cfDNA) was extracted from plasma specimens serially collected at specific time points (day 0, day 1, day 7, day 14, day 30, day 60) after LTx. The distribution of Gcf-DNA fragment sizes was measured using sequencing read lengths and quantified by using Y-chromosome capture methodology in seven sex-mismatched recipients. In the analysis of fragment size distribution, we observed Gcf-DNA exhibited smaller fragment sizes than the recipient-cfDNA. Based on this phenomenon, two fragment sizes (105-145 bp, 160-170 bp) of the cfDNA pool were extracted to enrich Gcf-DNA. Accordingly, the ratio of short fragments to long fragments (S/L-Frag) in cfDNA was calculated. A high S/L-Frag ratio pointed towards an early trend of graft injury when compared to two routine liver function enzymes (ALT and AST) and Gcf-DNA, and it significantly correlated with ALT (P < 0.0001) and AST (P < 0.0001) during full-blown rejection. In conclusion, we established the Gcf-DNA size profile in patients who have undergone living-related LTx and established a potential biomarker to monitor graft function after LTx.

[Data analysis of non-invasive prenatal testing based on special loci in cell-free fetal DNA].

OBJECTIVE To analyze the data of non-invasive prenatal testing based on specific loci of circulating cell-free fetal DNA (cffDNA). METHODS Selected loci of target chromosomes were analyzed by sequence capture and sequencing. Meanwhile, 600 loci were selected from other chromosomes for determining the concentration of cffDNA. RESULTS A total of 768 specific loci were captured on chromosomes 21 and 18, and used to determine whether the two were abnormal. When the minimum concentration of detected cffDNA was set at 3% and the threshold of Z score was set to [-6,6], the specificity of the analysis was 99.37% and the sensitivity was 100%. CONCLUSION A reliable, convenient and low-cost analytical method has been developed. The method requires less sequencing data for non-invasive prenatal testing, and can accurately detect abnormalities of fetal chromosomes 21 and 18, and simultaneously determine the concentration of cffDNA.

Sequencing data of cell-free DNA fragments in living-related liver transplantation for inborn errors of metabolism.

Graft derived cell-free DNA was recently reported as a non-invasive biomarker to detect graft damage or rejection after liver transplantation. There are a number of methods for quantification of Gcf-DNA, including quantitative-PCR, digital droplet PCR and massively parallel sequencing (next generation sequencing). Here we present the NGS data and fragment size distribution of cell-free DNA in the plasma of patients with inborn errors of metabolism who underwent living-related liver transplantation. For more insights please see Analysis of fragment size distribution of cell-free DNA: a potential noninvasive marker to monitor graft damage in living-related liver transplantation for inborn errors of metabolism. [1].

Effect quantification and value prediction of factors in noninvasive detection for specific fetal copy number variants by semiconductor sequencing.

BACKGROUND: The detection limit of noninvasive prenatal testing (NIPT) by next generation sequencing for any given fetal copy number variants (CNV) can be influenced by several factors. In this study, we quantified the effects and predicted the value of parameters for CNVs detection by NIPT. METHODS: Genomic DNA from patient's leucocytes with 3.16 Mb microdeletion in 22q11.21 was mixed with DNA from aborted fetal tissues without CNV at various concentrations by an enzyme digestion method. Abnormal DNA at 0% served as negative control. Sequencing of mixture samples (at 0%, 4%, 12%, and 20%) by Ion Proton Sequencer was performed at flow 500, with WISECONDOR as the pipeline in CNV-calling and bin of 500, 750 and 1,000 kb for counting unique reads. The parameters were evaluated with Box-Behnken design. The region with Z score ≦-3 was marked as a potential microdeletion. RESULTS: The equation of Z score depending on fetal fraction, unique read number and bin size was obtained by Box-Behnken design. The negative effect was quantified as the coefficient in the equation. The smallest values of these parameters were defined as 4 M unique read number, and 10.08% fetal DNA concentration at bin of 750 kb for detecting subchromosomal microdeletion of 3.16 Mb. CONCLUSION: The quantification of effect and value of parameters as well as the method used in this study can benefit the establishment of quality standards for CNVs detection and interpretation of CNVs detection results.

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy.

Chromosomal aneuploidy, one of the main causes leading to embryonic development arrest, implantation failure, or pregnancy loss, has been well documented in human embryos. Preimplantation genetic testing for aneuploidy (PGT-A) is a genetic test that significantly improves reproductive outcomes by detecting chromosomal abnormalities of embryos. Next-generation sequencing (NGS) provides a high-throughput and cost-effective approach for genetic analysis and has shown clinical applicability in PGT-A. Here, we present a rapid and low-cost semiconductor sequencing-based NGS method for screening of aneuploidy in embryos. The first step of the workflow is whole genome amplification (WGA) of the biopsied embryo specimen, followed by construction of sequencing library, and subsequent sequencing on the semiconductor sequencing system. Generally, for a PGT-A application, 24 samples can be loaded and sequenced on each chip generating 60-80 million reads at an average read length of 150 base pairs. The method provides a refined protocol for performing template amplification and enrichment of sequencing library, making the PGT-A detection reproducible, high-throughput, cost-efficient, and timesaving. The running time of this semiconductor sequencer is only 2-4 hours, shortening the turnaround time from receiving samples to issuing reports into 5 days. All these advantages make this assay an ideal method to detect chromosomal aneuploidies from embryos and thus, facilitate its wide application in PGT-A.

Raman profiling of embryo culture medium to identify aneuploid and euploid embryos.

OBJECTIVE: To develop and validate Raman metabolic footprint analysis to determine chromosome euploidy and aneuploidy in embryos fertilized in vitro. DESIGN: Retrospective study. SETTING: Academic hospital. PATIENT(S): Unselected assisted reproductive technology population. INTERVENTION(S): To establish the analysis protocol, spent embryo culture medium samples with known genetic outcomes from 87 human embryos were collected and measured with the use of Raman spectroscopy. Individual Raman spectra were analyzed to find biologic components contributing to either euploidy or aneuploidy. To validate the protocol via machine-learning algorithms, additional 1,107 Raman spectra from 123 embryo culture media (61 euploidy and 62 aneuploidy) were analyzed. MAIN OUTCOME MEASURE(S): Raman-based footprint profiling of spent culture media and preimplantation genetic testing for aneuploidy (PGT-A). RESULT(S): Mean-centered Raman spectra and principal component analysis showed differences in the footprints of euploid and aneuploid embryos growing in culture medium. Significant differences in Raman bands associated with small RNAs and lipids were also observed. Stacking classification based on k-nearest-neighbor, random forests, and extreme-gradient-boosting algorithms achieved an overall accuracy of 95.9% in correctly assigning either euploidy or aneuploidy based on Raman spectra, which was validated by PGT-A sequencing results. CONCLUSION(S): This study suggests that chromosomal abnormalities in embryos should lead to changes of metabolic footprints in embryo growth medium that can be detected by Raman spectroscopy. The ploidy status of embryos was analyzed by means of Raman-based footprint profiling of spent culture media and was consistent with PGT-A testing performed by next-generation sequencing.

Enrichment of the fetal fraction in non-invasive prenatal screening reduces maternal background interference.

Measurement of cell-free fetal DNA (cffDNA) is an indispensable process for non-invasive prenatal screening (NIPS). According to recent studies, cffDNA in maternal plasma can be enriched for various lengths of fragments, and a sufficient amount of cffDNA can effectively eliminate background interference on the part of maternal DNA. Therefore, we developed a simple and effective separation method, improved NIPS (iNIPS), that enriches the fetal fraction and improves the accuracy of NIPS for fetal aneuploid detection. We adopted a novel strategy to achieve enrichment of 125-135 bp cell-free DNA (cfDNA) by e-gel electrophoresis. To evaluate clinical performance, we compared NIPS and iNIPS results from 2153 retrospective clinical samples. Of the 22 samples with NIPS results of "no call", 17 samples were reclassified as "unaffected" (9 cases of chr13, 5 cases of chr18, and 3 cases of chr21); 2 samples remained classified as "no call" (1 case of chr18 and 1 case of chr21); and 3 samples were identified as T21 by iNIPS. The average increase in abundance of cfDNA fragments of 125-135 bp was 2.5 times, and the average decrease in maternal background interference was 1.3 times. On this basis, the detection of fetal aneuploidy was highly improved with the fetal fraction as low as 2%; iNIPS achieved 100% sensitivity and 99.90% specificity in retrospective samples.

Identification of pathways and genes associated with cerebral palsy.

Cerebral palsy (CP) is a non-progressive neurological disease, of which susceptibility is linked to genetic and environmental risk factors. More and more studies have shown that CP might be caused by multiple genetic factors, similar to other neurodevelopmental disorders. Due to the high genetic heterogeneity of CP, we focused on investigating related molecular pathways. Ten children with CP were collected for whole-exome sequencing by next-generation sequencing (NGS) technology. Customized processes were used to identify potential pathogenic pathways and variants. Three pathways (axon guidance, transmission across chemical synapses, protein-protein interactions at synapses) with twenty-three genes were identified to be highly correlated with CP. This study showed that the three pathways associated with CP might be the molecular mechanism of pathogenesis. These findings could provide useful clues for developing pathway-based pharmacotherapies. Further studies are required to confirm potential roles for these pathways in the pathogenesis of CP.

The comparison of the performance of four whole genome amplification kits on ion proton platform in copy number variation detection.

With the development and clinical application of genomics, more and more concern is focused on single-cell sequencing. In the process of single-cell sequencing, whole genome amplification is a key step to enrich sample DNA. Previous studies have compared the performance of different whole genome amplification (WGA) strategies on Illumina sequencing platforms, but there is no related research aimed at Ion Proton platform, which is also a popular next-generation sequencing platform. Here by amplifying cells from six cell lines with different karyotypes, we estimated the data features of four common commercial WGA kits (PicoPLEX WGA Kit, GenomePlex Single Cell Whole Genome Amplification Kit, MALBAC Single Cell Whole Genome Amplification Kit, and REPLI-g Single Cell Kit), including median absolute pairwise difference, uniformity, reproducibility, and fidelity, and examined their performance of copy number variation detection. The results showed that both MALBAC and PicoPLEX could yield high-quality data and had high reproducibility and fidelity; and as for uniformity, PicoPLEX was slightly superior to MALBAC.

An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing.

Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing.

NeSSM: a Next-generation Sequencing Simulator for Metagenomics.

BACKGROUND: Metagenomics can reveal the vast majority of microbes that have been missed by traditional cultivation-based methods. Due to its extremely wide range of application areas, fast metagenome sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of metagenomics analysis tools. RESULTS: We present here a customizable metagenome simulation system: NeSSM (Next-generation Sequencing Simulator for Metagenomics). Combining complete genomes currently available, a community composition table, and sequencing parameters, it can simulate metagenome sequencing better than existing systems. Sequencing error models based on the explicit distribution of errors at each base and sequencing coverage bias are incorporated in the simulation. In order to improve the fidelity of simulation, tools are provided by NeSSM to estimate the sequencing error models, sequencing coverage bias and the community composition directly from existing metagenome sequencing data. Currently, NeSSM supports single-end and pair-end sequencing for both 454 and Illumina platforms. In addition, a GPU (graphics processing units) version of NeSSM is also developed to accelerate the simulation. By comparing the simulated sequencing data from NeSSM with experimental metagenome sequencing data, we have demonstrated that NeSSM performs better in many aspects than existing popular metagenome simulators, such as MetaSim, GemSIM and Grinder. The GPU version of NeSSM is more than one-order of magnitude faster than MetaSim. CONCLUSIONS: NeSSM is a fast simulation system for high-throughput metagenome sequencing. It can be helpful to develop tools and evaluate strategies for metagenomics analysis and it's freely available for academic users at http://cbb.sjtu.edu.cn/~ccwei/pub/software/NeSSM.php.

MetaBinG: using GPUs to accelerate metagenomic sequence classification.

Metagenomic sequence classification is a procedure to assign sequences to their source genomes. It is one of the important steps for metagenomic sequence data analysis. Although many methods exist, classification of high-throughput metagenomic sequence data in a limited time is still a challenge. We present here an ultra-fast metagenomic sequence classification system (MetaBinG) using graphic processing units (GPUs). The accuracy of MetaBinG is comparable to the best existing systems and it can classify a million of 454 reads within five minutes, which is more than 2 orders of magnitude faster than existing systems. MetaBinG is publicly available at http://cbb.sjtu.edu.cn/~ccwei/pub/software/MetaBinG/MetaBinG.php.