Search Results (4)

Microbial single-cell genomics (SCG) provides access to the genomes of rare and uncultured microorganisms and is a complementary method to metagenomics. Due to the femtogram-levels of DNA in a single microbial cell, sequencing the genome requires whole genome amplification (WGA) as a preliminary step. However, the most common WGA method, multiple displacement amplification (MDA), is known to be costly and biased against specific genomic regions, preventing high-throughput applications and resulting in uneven genome coverage. Thus, obtaining high-quality genomes from many taxa, especially minority members of microbial communities, becomes difficult. Here, we present a volume reduction approach that significantly reduces costs while improving genome coverage and uniformity of DNA amplification products in standard 384-well plates. Our results demonstrate that further volume reduction in specialized and complex setups (e.g., microfluidic chips) is likely unnecessary to obtain higher-quality microbial genomes. This volume reduction method makes SCG more feasible for future studies, thus helping to broaden our knowledge on the diversity and function of understudied and uncharacterized microorganisms in the environment. Full article

Controlled ovarian stimulation is a necessary step in some assisted reproductive procedures allowing a higher collection of female gametes. However, consequences of this stimulation for the gamete or the offspring have been shown in several mammals. Most studies used comparisons between oocytes from different donors, which may contribute to different responses. In this work, we use the bovine model in which each animal serves as its own control. DNA methylation profiles were obtained by single-cell whole-genome bisulfite sequencing of oocytes from pre-ovulatory unstimulated follicles compared to oocytes from stimulated follicles. Results show that the global percentage of methylation was similar between groups, but the percentage of methylation was lower for non-stimulated oocytes in the imprinted genes APEG3, MEG3, and MEG9 and higher in TSSC4 when compared to stimulated oocytes. Differences were also found in CGI of imprinted genes: higher methylation was found among non-stimulated oocytes in MEST (PEG1), IGF2R, GNAS (SCG6), KvDMR1 ICR UMD, and IGF2. In another region around IGF2, the methylation percentage was lower for non-stimulated oocytes when compared to stimulated oocytes. Data drawn from this study might help to understand the molecular reasons for the appearance of certain syndromes in assisted reproductive technologies-derived offspring. Full article

Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and missing bases, severely limit its application. Here, we present a deep learning framework, RDAClone, to recover genotype matrices from noisy data with an extended robust deep autoencoder, cluster cells into subclones by the Louvain-Jaccard method, and further infer evolutionary relationships between subclones by the minimum spanning tree. Studies on both simulated and real datasets demonstrate its robustness and superiority in data denoising, cell clustering, and evolutionary tree reconstruction, particularly for large datasets. Full article

Since the time when detection of gene expression in single cells by microarrays to the Next Generation Sequencing (NGS) enabled Single Cell Genomics (SCG), it has played a pivotal role to understand and elucidate the functional role of cellular heterogeneity. Along this journey to becoming a key player in the capture of the individuality of cells, SCG overcame many milestones, including scale, speed, sensitivity and sample costs (4S). There have been many important experimental and computational innovations in the efficient analysis and interpretation of SCG data. The increasing role of AI in SCG data analysis has further enhanced its applicability in building models for clinical intervention. Furthermore, SCG has been instrumental in the delineation of the role of cellular heterogeneity in specific diseases, including cancer and infectious diseases. The understanding of the role of differential immune responses in driving coronavirus disease-2019 (COVID-19) disease severity and clinical outcomes has been greatly aided by SCG. With many variants of concern (VOC) in sight, it would be of great importance to further understand the immune response specificity vis-a-vis the immune cell repertoire, the identification of novel cell types, and antibody response. Given the potential of SCG to play an integral part in the multi-omics approach to the study of the host–pathogen interaction and its outcomes, our review attempts to highlight its strengths, its implications for infectious disease biology, and its current limitations. We conclude that the application of SCG would be a critical step towards future pandemic preparedness. Full article