In conclusion, the robustness of DNS and STR to collect CFCs from peripheral maternal blood for the first time coupled with deep sequencing technique demonstrates the possibility of comprehensive non-invasive prenatal testing of genetic disorders using isolated CFCs.

Research based on a strategy of single-cell low-coverage whole genome sequencing (SLWGS) has enabled better reproducibility and accuracy for detection of copy number variations (CNVs). The whole genome amplification (WGA) method and sequencing platform are critical factors for successful SLWGS (<0.1 × coverage). In this study, we compared single cell and multiple cells sequencing data produced by the HiSeq2000 and Ion Proton platforms using two WGA kits and then comprehensively evaluated the GC-bias, reproducibility, uniformity and CNV detection among different experimental combinations. Our analysis demonstrated that the PicoPLEX WGA Kit resulted in higher reproducibility, lower sequencing error frequency but more GC-bias than the GenomePlex Single Cell WGA Kit (WGA4 kit) independent of the cell number on the HiSeq2000 platform. While on the Ion Proton platform, the WGA4 kit (both single cell and multiple cells) had higher uniformity and less GC-bias but lower reproducibility than those of the PicoPLEX WGA Kit. Moreover, on these two sequencing platforms, depending on cell number, the performance of the two WGA kits was different for both sensitivity and specificity on CNV detection. The results can help researchers who plan to use SLWGS on single or multiple cells to select appropriate experimental conditions for their applications.

The libraries generated by high-throughput single cell RNA-sequencing (scRNA-seq) platforms such as the Chromium from 10× Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use these data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3′ libraries consisting of over 70 000 cells generated on the 10× Genomics Chromium platform. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and the detection of genes, cell barcodes, Unique Molecular Identifiers. The performance of the NextSeq 500 was also similarly comparable to the MGISEQ-2000 based on the same metrics. Data generated by both sequencing platforms yielded similar analytical outcomes for general single-cell analysis. The performance of the NextSeq 500 and MGISEQ-2000 were also comparable for the deconvolution of multiplexed cell pools via variant calling, and detection of guide RNA (gRNA) from a pooled CRISPR single-cell screen. Our study provides a benchmark for high-capacity sequencing platforms applied to high-throughput scRNA-seq libraries.

AbstractThe libraries generated by high-throughput single cell RNA-sequencing platforms such as the Chromium from 10X Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use this data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3’ libraries consisting of over 70,000 cells. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and cell, UMI, and gene detection. However, compared with the NextSeq 500, the MGISEQ-2000 platform performs consistently better, identifying more cells, genes, and UMIs at equalised read depth. We were able to call an additional 1,065,659 SNPs from sequence data generated by the BGI platform, enabling an additional 14% of cells to be assigned to the correct donor from a multiplexed library. However, both the NextSeq 500 and MGISEQ-2000 detected similar frequencies of gRNAs from a pooled CRISPR single cell screen. Our study provides a benchmark for high capacity sequencing platforms applied to high-throughput single cell RNA-seq libraries.

Background: Preimplantation genetic test for monogenic disorders (PGT-M) has been used to select genetic disease-free embryos for implantation during in vitro fertilization (IVF) treatment. However, embryos tested by PGT-M have risks of harboring chromosomal aneuploidy. Hence, a universal method to detect monogenic diseases and genomic imbalances is required.Methods: Here, we report a novel PGT-A/M procedure allowing simultaneous detection of monogenic diseases and genomic imbalances in one experiment. Library was prepared in a special way that multiplex polymerase chain reaction (PCR) was integrated into the process of whole genome amplification. The resulting library was used for one-step low-pass whole genome sequencing (WGS) and high-depth target enrichment sequencing (TES).Results: The TAGs-seq PGT-A/M was first validated with genomic DNA (gDNA) and the multiple displacement amplification (MDA) products of a cell line. Over 90% of sequencing reads covered the whole-genome region with around 0.3–0.4 × depth, while around 5.4%–7.3% of reads covered target genes with &gt;10000 × depth. Then, for clinical validation, 54 embryos from 8 women receiving PGT-M of β-thalassemia were tested by the TAGs-seq PGT-A/M. In each embryo, an average of 20.0 million reads with 0.3 × depth of the whole-genome region was analyzed for genomic imbalance, while an average of 0.9 million reads with 11260.0 × depth of the target gene HBB were analyzed for β-thalassemia. Eventually, 18 embryos were identified with genomic imbalance with 81.1% consistency to karyomapping results. 10 embryos contained β-thalassemia with 100% consistency to conventional PGT-M method.Conclusion: TAGs-seq PGT-A/M simultaneously detected genomic imbalance and monogenic disease in embryos without dramatic increase of sequencing data output.

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