Comparison of SureSelect and Nextera Exome Capture Performance in Single-Cell Sequencing.

BACKGROUND: Advances in single-cell sequencing provide unprecedented opportunities for clinical examination of circulating tumor cells, cancer stem cells, and other rare cells responsible for disease progression and drug resistance. On the genomic level, single-cell whole exome sequencing (scWES) started to gain popularity with its unique potentials in characterizing mutational landscapes at a single-cell level. Currently, there is little known about the performance of different exome capture kits in scWES. Nextera rapid capture (NXT; Illumina, Inc.) has been the only exome capture kit recommended for scWES by Fluidigm C1, a widely accessed system in single-cell preparation. RESULTS: In this study, we compared the performance of NXT following Fluidigm's protocol with Agilent SureSelectXT Target Enrichment System (AGL), another exome capture kit widely used for bulk sequencing. We created DNA libraries of 192 single cells isolated from spheres grown from a melanoma specimen using Fluidigm C1. Twelve high-yield cells were selected to perform dual-exome capture and sequencing using AGL and NXT in parallel. After mapping and coverage analysis, AGL outperformed NXT in coverage uniformity, mapping rates of reads, exome capture rates, and low PCR duplicate rates. For germline variant calling, AGL achieved better performance in overlap with known variants in dbSNP and transition-transversion ratios. Using calls from high coverage bulk sequencing from blood DNA as the golden standard, AGL-based scWES demonstrated high positive predictive values, and medium to high sensitivity. Lastly, we evaluated somatic mutation calling by comparing single-cell data with the matched blood sequence as control. On average, 300 mutations were identified in each cell. In 10 of 12 cells, higher numbers of mutations were identified using AGL than NXT, probably caused by coverage depth. When mutations are adequately covered in both AGL and NXT data, the two methods showed very high concordance (93-100% per cell). CONCLUSIONS: Our results suggest that AGL can also be used for scWES when there is sufficient DNA, and it yields better data quality than the current Fluidigm's protocol using NXT.

[Value of 18F-FDG PET/CT Imaging in Secondary Extramedullary Infiltration of Multiple Myeloma].

OBJECTIVE: To investigate the characteristics of 18F-FDG PET/CT images of multiple myeloma secondary extramedullary infiltration in order to improve recognition. METHODS: Twenty-one patients with multiple myeloma secondary extramedullary infiltration confirmed by pathology or follow-up from January 2012 to October 2020 in the First Affiliated Hospital of University of Science and Technology of China were retrospectively analyzed. All the patients underwent 18F-FDG PET/CT imaging before treatment, and the PET/CT characteristics of extramedullary infiltration and bone marrow were analyzed. RESULTS: Twenty-one patients included 12 males and 9 females, aged from 41 to 77 years old, with an average of 58.3±10.0; 9 cases of extramedullary infiltration involving lymph nodes; lung, stomach, spleen, and kidney were involved respectively in 2 cases; retroperitoneal, right auricle, subcutaneous nodule, and spinal meninges involvement were reported in each one case respectively. The maximum SUVmax value of extra-medullary lesions was 21.2, the minimum value was 2.1, and mean was 7.7±5.3. The maximum SUVmax value of bone marrow was 33.5, the minimum was 2.4, and mean was 6.6±3.6. There was no statistically significant difference in SUVmax value between extra-medullary lesions and bone marrow (Z=-1.195, P=0.232). CONCLUSION: 18F-FDG PET/CT not only has a good diagnostic value for multiple myeloma, but also a good evaluation value for secondary extramedullary infiltration, which provides reference for clinical treatment and prognosis.

An adaptive method of defining negative mutation status for multi-sample comparison using next-generation sequencing.

BACKGROUND: Multi-sample comparison is commonly used in cancer genomics studies. By using next-generation sequencing (NGS), a mutation's status in a specific sample can be measured by the number of reads supporting mutant or wildtype alleles. When no mutant reads are detected, it could represent either a true negative mutation status or a false negative due to an insufficient number of reads, so-called "coverage". To minimize the chance of false-negative, we should consider the mutation status as "unknown" instead of "negative" when the coverage is inadequately low. There is no established method for determining the coverage threshold between negative and unknown statuses. A common solution is to apply a universal minimum coverage (UMC). However, this method relies on an arbitrarily chosen threshold, and it does not take into account the mutations' relative abundances, which can vary dramatically by the type of mutations. The result could be misclassification between negative and unknown statuses. METHODS: We propose an adaptive mutation-specific negative (MSN) method to improve the discrimination between negative and unknown mutation statuses. For a specific mutation, a non-positive sample is compared with every known positive sample to test the null hypothesis that they may contain the same frequency of mutant reads. The non-positive sample can only be claimed as "negative" when this null hypothesis is rejected with all known positive samples; otherwise, the status would be "unknown". RESULTS: We first compared the performance of MSN and UMC methods in a simulated dataset containing varying tumor cell fractions. Only the MSN methods appropriately assigned negative statuses for samples with both high- and low-tumor cell fractions. When evaluated on a real dual-platform single-cell sequencing dataset, the MSN method not only provided more accurate assessments of negative statuses but also yielded three times more available data after excluding the "unknown" statuses, compared with the UMC method. CONCLUSIONS: We developed a new adaptive method for distinguishing unknown from negative statuses in multi-sample comparison NGS data. The method can provide more accurate negative statuses than the conventional UMC method and generate a remarkably higher amount of available data by reducing unnecessary "unknown" calls.

Ultradeep sequencing differentiates patterns of skin clonal mutations associated with sun-exposure status and skin cancer burden.

In ultraviolet (UV) radiation-exposed skin, mutations fuel clonal cell growth. The relationship between UV exposure and the accumulation of clonal mutations (CMs) and the correlation between CMs and skin cancer risk are largely unexplored. We characterized 450 individual-matched sun-exposed (SE) and non-SE (NE) normal human skin samples. The number and relative contribution of CMs were significantly different between SE and NE areas. Furthermore, we identified hotspots in TP53, NOTCH1, and GRM3 where mutations were significantly associated with UV exposure. In the normal skin from patients with cutaneous squamous cell carcinoma, we found that the cancer burden was associated with the UV-induced mutations, with the difference mostly conferred by the low-frequency CMs. These findings provide previously unknown information on UV's carcinogenic effect and pave the road for future development of quantitative assessment of subclinical UV damage and skin cancer risk.

Association of MIF-173G/C and MBL2 codon 54 gene polymorphisms with rheumatoid arthritis: a meta-analysis.

The aim of this study was to evaluate the association between macrophage migration inhibitory factor (MIF) -173G/C (rs755622), mannose-binding lectin (MBL2) exon 1 codon 54 (rs1800450) gene polymorphisms and rheumatoid arthritis (RA) susceptibility in ethnically different populations. A meta-analysis was conducted (allelic contrast, the additive model, the dominant model and the recessive model) on the MIF-173G/C polymorphism across five studies (four European and one Asian studies), and the MBL2 codon 54 polymorphism with five studies (four Asian and one European studies), respectively. Meta-analysis indicated an association between the MIF-173G/C in all study subjects in allelic contrast (OR=1.19, 95%CI: 1.05-1.35, P=0.001), the additive model (OR=1.68, 95CI: 1.13-2.49, P=0.001), the dominant model (OR=1.17, 95CI: 1.01-1.35, P=0.003), the recessive model (OR=1.63, 95CI: 1.10-2.42, P=0.001). While stratified by ethnicity with European populations, an association was found in allelic contrast (OR=1.20, 95CI: 1.04-1.38, P=0.002), the additive model (OR=1.85, 95CI: 1.19-2.88, P=0.001), the dominant model (OR=1.20, 95CI: 1.02-1.41, P=0.003). With respect to MBL2 codon 54 polymorphism and RA, no association was found in all study subjects in all comparisons, but there was an association while stratified by ethnicity with Asian populations in the dominant model (OR=1.50, 95CI: 1.01-2.23, P=0.007). In conclusion, the present study suggests that the MIF-173G/C polymorphism is associated with RA susceptibility, but the MBL2 codon 54 polymorphism is not associated with RA.