A short review of variants calling for single-cell-sequencing data with applications

Droplet-based single-cell sequencing: Strategies and applications

Notable advancements in single-cell omics technologies have not only addressed longstanding challenges but also enabled unprecedented studies of cellular heterogeneity with unprecedented resolution and scale. These strides have led to groundbreaking insights into complex biological systems, paving the way for a more profound comprehension of human biology and diseases. The droplet microfluidic technology has become a crucial component in many single-cell sequencing workflows in terms of throughput, cost-effectiveness, and automation. Utilizing a microfluidic chip to encapsulate and profile individual cells within droplets has significantly improved single-cell research. Therefore, this review aims to comprehensively elaborate the droplet microfluidics-assisted omics methods from a single-cell perspective. The strategies for using droplet microfluidics in the realms of genomics, epigenomics, transcriptomics, and proteomics analyses are first introduced. On this basis, the focus then turns to the latest applications of this technology in different sequencing patterns, including mono- and multi-omics. Finally, the challenges and further perspectives of droplet-based single-cell sequencing in both foundational research and commercial applications are discussed.

Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network

Nanopore sequencing is promising because of its long read length and high speed. During sequencing, a strand of DNA/RNA passes through a biological nanopore, which causes the current in the pore to fluctuate. During basecalling, context-dependent current measurements are translated into the base sequence of the DNA/RNA strand. Accurate and fast basecalling is vital for downstream analyses such as genome assembly and detecting single-nucleotide polymorphisms and genomic structural variants. However, owing to the various changes in DNA/RNA molecules, noise during sequencing, and limitations of basecalling methods, accurate basecalling remains a challenge. In this paper, we propose Causalcall, which uses an end-to-end temporal convolution-based deep learning model for accurate and fast nanopore basecalling. Developed on a temporal convolutional network (TCN) and a connectionist temporal classification decoder, Causalcall directly identifies base sequences of varying lengths from current measurements in long time series. In contrast to the basecalling models using recurrent neural networks (RNNs), the convolution-based model of Causalcall can speed up basecalling by matrix computation. Experiments on multiple species have demonstrated the great potential of the TCN-based model to improve basecalling accuracy and speed when compared to an RNN-based model. Besides, experiments on genome assembly indicate the utility of Causalcall in reference-based genome assembly.

Improved Molecular Diagnosis of McCune-Albright Syndrome and Bone Fibrous Dysplasia by Digital PCR

McCune-Albright syndrome (MAS) is a rare congenital disorder characterized by the association of endocrine and nonendocrine anomalies caused by somatic activating variants of GNAS. The mosaic state of variants makes the clinical presentation extremely heterogeneous depending on involved tissues. Biological samples bearing a low level of mosaicism frequently lead to false-negative results with an underestimation of causative molecular alterations, and the analysis of biopsies is often needed to obtain a molecular diagnosis. To date, no reliable analytical method for the noninvasive testing of blood is available. This study was aimed at validating a novel and highly sensitive technique, the digital PCR (dPCR), to increase the detection rate of GNAS alterations in patients with a clinical suspicion of MAS and, in particular, in blood. We screened different tissues (blood, bone, cutis, ovary, and ovarian cyst) collected from 54 MAS patients by different technical approaches. Considering blood, Sanger was unable to detect mutations, the allele-specific PCR and the co-amplification at lower denaturation temperature had a 9.1% and 18.1% detection rate, respectively, whereas the dPCR reached a 37.8% detection rate. In conclusion, the dPCR resulted in a cost-effective, reliable, and rapid method allowing the selective amplification of low-frequency variants and able to improve GNAS mutant allele detection, especially in the blood.

Combined Genome and Transcriptome (G&T) Sequencing of Single Cells

The simultaneous examination of a single cell's genome and transcriptome presents scientists with a powerful tool to study genetic variability and its effect on gene expression. In this chapter, we describe the library generation method for combined genome and transcriptome sequencing (G&T-seq) originally described by Macaulay et al. (Nat Protoc 11(11):2081-2103, 2016; Nat Methods 12(6):519-522, 2015). This includes some alterations we made to improve robustness of this process for both the novice user and laboratories that want to deploy this method at scale. Using this method, genomic DNA and full-length mRNA from single cells are separated, amplified, and converted into Illumina sequencer-compatible sequencing libraries.