Preimplantation genetic diagnosis/screening (PGD/PGS) aims to help couples lower the risks of transmitting genetic defects to their offspring, implantation failure, and/or miscarriage during in vitro fertilization (IVF) cycles. However, it is still being debated with regard to the practicality and diagnostic accuracy of PGD/PGS due to the concern of invasive biopsy and the potential mosaicism of embryos. Recently, several non-invasive and high-throughput assays have been developed to help overcome the challenges encountered in the conventional invasive biopsy and low-throughput analysis in PGD/PGS. In this mini-review, we will summarize the recent progresses of these new methods for PGD/PGS and discuss their potential applications in IVF clinics.

Peripheral nerve injury leads to various injury-induced responses in sensory neurons including physiological pain, neuronal cell death, and nerve regeneration. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis of mouse nonpeptidergic nociceptors (NP), peptidergic nociceptors (PEP), and large myelinated sensory neurons (LM) under both control and injury conditions at 3 days after sciatic nerve transection (SNT). After performing principle component and weighted gene co-expression network analysis, we categorized dorsal root ganglion (DRG) neurons into different subtypes and discovered co-regulated injury-response genes including novel regeneration associated genes (RAGs) in association with neuronal development, protein translation and cytoplasm transportation. In addition, we found significant up-regulation of the genes associated with cell death such as Pdcd2 in a subset of NP neurons after axotomy, implicating their actions in neuronal cell death upon nerve injury. Our study revealed the distinctive and sustained heterogeneity of transcriptomic responses to injury at single neuron level, implicating the involvement of different gene regulatory networks in nerve regeneration, neuronal cell death and neuropathy in different population of DRG neurons.

Machine vision-based structural health monitoring is gaining popularity due to the rich information one can extract from video and images. However, the extraction of characteristic parameters from images often requires manual intervention, thereby limiting its scalability and effectiveness. In contrast, deep learning overcomes the aforementioned shortcoming in that it can autonomously extract feature parameters (e.g. structural damage) from image datasets. Therefore, this study aims to validate the use of machine vision and deep learning for structural health monitoring by focusing on a particular application of detecting bolt loosening. First, a dataset that contains 300 images was collected. The dataset includes two bolt states, namely, tight and loosened. Second, a faster region-based convolutional neural network was trained and evaluated. The test results showed that the average precision of bolt damage detection is 0.9503. Thereafter, bolts were loosened to various screw heights, and images obtained from different angles, lighting conditions, and vibration conditions were identified separately. The trained model was then employed to validate that bolt loosening could be detected with sufficient accuracy using various types of images. Finally, the trained model was connected with a webcam to realize real-time bolt loosening damage monitoring.

Lung adenocarcinoma (LUAD) harboring EGFR mutations prevails in Asian population. However, the inter-patient and intra-tumor heterogeneity has not been addressed at single-cell resolution. Here we performed single-cell RNA sequencing (scRNA-seq) of total 125,674 cells from seven stage-I/II LUAD samples harboring EGFR mutations and five tumor-adjacent lung tissues. We identified diverse cell types within the tumor microenvironment (TME) in which myeloid cells and T cells were the most abundant stromal cell types in tumors and adjacent lung tissues. Within tumors, accompanied by an increase in CD1C⁺ dendritic cells, the tumor-associated macrophages (TAMs) showed pro-tumoral functions without signature gene expression of defined M1 or M2 polarization. Tumor-infiltrating T cells mainly displayed exhausted and regulatory T-cell features. The adenocarcinoma cells can be categorized into different subtypes based on their gene expression signatures in distinct pathways such as hypoxia, glycolysis, cell metabolism, translation initiation, cell cycle, and antigen presentation. By performing pseudotime trajectory, we found that ELF3 was among the most upregulated genes in more advanced tumor cells. In response to secretion of inflammatory cytokines (e.g., IL1B) from immune infiltrates, ELF3 in tumor cells was upregulated to trigger the activation of PI3K/Akt/NF-κB pathway and elevated expression of proliferation and anti-apoptosis genes such as BCL2L1 and CCND1. Taken together, our study revealed substantial heterogeneity within early-stage LUAD harboring EGFR mutations, implicating complex interactions among tumor cells, stromal cells and immune infiltrates in the TME.

Background

Single-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to understand the direct correlation of DNA methylation and gene expression within single-cells. Due to large cell-to-cell variability and the lack of direct measurements of transcriptome and methylome of the same cell, the association is still unclear.

Results

Here, we describe a novel method (scMT-seq) that simultaneously profiles both DNA methylome and transcriptome from the same cell. In sensory neurons, we consistently identify transcriptome and methylome heterogeneity among single cells but the majority of the expression variance is not explained by proximal promoter methylation, with the exception of genes that do not contain CpG islands. By contrast, gene body methylation is positively associated with gene expression for only those genes that contain a CpG island promoter. Furthermore, using single nucleotide polymorphism patterns from our hybrid mouse model, we also find positive correlation of allelic gene body methylation with allelic expression.

Conclusions

Our method can be used to detect transcriptome, methylome, and single nucleotide polymorphism information within single cells to dissect the mechanisms of epigenetic gene regulation.

Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell RNA sequencing. Based on single-nucleotide variants in human blastomere messenger RNAs and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos.

Whether human induced pluripotent stem cells (hiPSCs) are epigenetically identical to human embryonic stem cells (hESCs) has been debated in the stem cell field. In this study, we analyzed DNA methylation patterns in a large number of hiPSCs (n = 114) and hESCs (n = 155), and identified a panel of 82 CpG methylation sites that can distinguish hiPSCs from hESCs with high accuracy. We show that 12 out of the 82 CpG sites were subject to hypermethylation in part by DNMT3B. Notably, DNMT3B contributes directly to aberrant hypermethylation and silencing of the signature gene, TCERG1L. Overall, we conclude that DNMT3B is involved in a wave of de novo methylation during reprogramming, a portion of which contributes to the unique hiPSC methylation signature. These 82 CpG methylation sites may be useful as biomarkers to distinguish between hiPSCs and hESCs.

Immunodeficiency, centromeric instability and facial anomalies type I (ICF1) syndrome is a rare genetic disease caused by mutations in DNA methyltransferase (DNMT) 3B, a de novo DNA methyltransferase. However, the molecular basis of how DNMT3B deficiency leads to ICF1 pathogenesis is unclear. Induced pluripotent stem cell (iPSC) technology facilitates the study of early human developmental diseases via facile in vitro paradigms. Here, we generate iPSCs from ICF Type 1 syndrome patient fibroblasts followed by directed differentiation of ICF1-iPSCs to mesenchymal stem cells (MSCs). By performing genome-scale bisulfite sequencing, we find that DNMT3B-deficient iPSCs exhibit global loss of non-CG methylation and select CG hypomethylation at gene promoters and enhancers. Further unbiased scanning of ICF1-iPSC methylomes also identifies large megabase regions of CG hypomethylation typically localized in centromeric and subtelomeric regions. RNA sequencing of ICF1 and control iPSCs reveals abnormal gene expression in ICF1-iPSCs relevant to ICF syndrome phenotypes, some directly associated with promoter or enhancer hypomethylation. Upon differentiation of ICF1 iPSCs to MSCs, we find virtually all CG hypomethylated regions remained hypomethylated when compared with either wild-type iPSC-derived MSCs or primary bone-marrow MSCs. Collectively, our results show specific methylome and transcriptome defects in both ICF1-iPSCs and differentiated somatic cell lineages, providing a valuable stem cell system for further in vitro study of the molecular pathogenesis of ICF1 syndrome. GEO accession number: GSE46030.

Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell RNA sequencing. Based on single-nucleotide variants in human blastomere messenger RNAs and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos.