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Here, we review single-cell sequencing techniques for individual and multiomics profiling in single cells. We mainly describe single-cell genomic, epigenomic, and transcriptomic methods, and examples of their applications. For the integration of multilayered data sets, such as the transcriptome data derived from single-cell RNA sequencing and chromatin accessibility data derived from single-cell ATAC-seq, there are several computational integration methods. We also describe single-cell experimental methods for the simultaneous measurement of two or more omics layers. We can achieve a detailed understanding of the basic molecular profiles and those associated with disease in each cell by utilizing a large number of single-cell sequencing techniques and the accumulated data sets.Single-cell sequencing: Greater insight through integrated dataCombining data from different single-cell sequencing techniques could greatly improve understanding of the molecular profiles associated with disease. Sequencing studies provide valuable insights into diseased and healthy states at a single-cell level, for example the evolutionary paths of brain tumors and cancerous mutations. Ayako Suzuki at the University of Tokyo in Chiba, Japan, and co-workers examined the challenges of integrating data from various experimental and computational single-cell sequencing methods. These methods usually determine the genomic, epigenomic (DNA modifications) or transcriptomic (messenger RNAs) state of a cell, and can be combined to create a detailed picture. Other ‘multiomics’ techniques provide multilayered information from the same cell. The researchers recommend detailed analysis of individual data layers prior to integration, and highlight emerging techniques that analyze larger tissue sections, thus retaining the temporal and spatial information around a cell.

PIWI-interacting RNAs (piRNAs) of between approximately 24 and 31 nucleotides in length guide PIWI proteins to silence transposons in animal gonads, thereby ensuring fertility 1 . In the biogenesis of piRNAs, PIWI proteins are first loaded with 5′-monophosphorylated RNA fragments called pre-pre-piRNAs, which then undergo endonucleolytic cleavage to produce pre-piRNAs 1 , 2 . Subsequently, the 3′-ends of pre-piRNAs are trimmed by the exonuclease Trimmer (PNLDC1 in mouse) 3 – 6 and 2′- O -methylated by the methyltransferase Hen1 (HENMT1 in mouse) 7 – 9 , generating mature piRNAs. It is assumed that the endonuclease Zucchini (MitoPLD in mouse) is a major enzyme catalysing the cleavage of pre-pre-piRNAs into pre-piRNAs 10 – 13 . However, direct evidence for this model is lacking, and how pre-piRNAs are generated remains unclear. Here, to analyse pre-piRNA production, we established a Trimmer-knockout silkworm cell line and derived a cell-free system that faithfully recapitulates Zucchini-mediated cleavage of PIWI-loaded pre-pre-piRNAs. We found that pre-piRNAs are generated by parallel Zucchini-dependent and -independent mechanisms. Cleavage by Zucchini occurs at previously unrecognized consensus motifs on pre-pre-piRNAs, requires the RNA helicase Armitage, and is accompanied by 2′- O -methylation of pre-piRNAs. By contrast, slicing of pre-pre-piRNAs with weak Zucchini motifs is achieved by downstream complementary piRNAs, producing pre-piRNAs without 2′- O -methylation. Regardless of the endonucleolytic mechanism, pre-piRNAs are matured by Trimmer and Hen1. Our findings highlight multiplexed processing of piRNA precursors that supports robust and flexible piRNA biogenesis. A silkworm model recapitulates key steps of Zucchini-mediated cleavage of pre-pre-piRNA and provides insights into Zucchini-mediated and -independent pathways that generate pre-piRNAs, which converge to a common piRNA maturation step.

Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor microenvironment. In this environment, cancer cells change their expression profiles to fight against circumstantial stresses, allowing their progression in the challenging tissue space. Technological advancements of spatial omics have had substantial influence on cancer genomics. This technical progress, especially that occurring in the spatial transcriptome, has been drastic and rapid. Here, we describe the latest spatial analytical technologies that have allowed omics feature characterization to retain their spatial and histopathological information in cancer tissues. Several spatial omics platforms have been launched, and the latest platforms finally attained single‐cell level or even higher subcellular level resolution. We discuss several key papers elucidating the initial utility of the spatial analysis. In fact, spatial transcriptome analyses reveal comprehensive omics characteristics not only in cancer cells but also their surrounding cells, such as tumor infiltrating immune cells and cancer‐associated fibroblasts. We also introduce several spatial omics platforms. We describe our own attempts to investigate molecular events associated with cancer progression. Furthermore, we discuss the next challenges in analyzing the multiomics status of cells, including their morphology and location. These novel technologies, in conjunction with spatial transcriptome analysis and, more importantly, with histopathology, will elucidate even novel key aspects of the intratumor heterogeneity of cancers. Such enhanced knowledge is expected to open a new path for overcoming therapeutic resistance and eventually to precisely stratify patients. Here, we have introduced the latest spatial analytical technologies, which has allowed characterization of omics features retaining their spatial and histopathological information in cancer tissues. We also summarized several key papers elucidating the utility of the spatial omics analysis for cancer research.

Non-pial neocortical astrocytes have historically been thought to comprise largely a nondiverse population of protoplasmic astrocytes. Here we show that astrocytes of the mouse somatosensory cortex manifest layer-specific morphological and molecular differences. Two- and three-dimensional observations revealed that astrocytes in the different layers possess distinct morphologies as reflected by differences in cell orientation, territorial volume, and arborization. The extent of ensheathment of synaptic clefts by astrocytes in layer II/III was greater than that by those in layer VI. Moreover, differences in gene expression were observed between upper-layer and deep-layer astrocytes. Importantly, layer-specific differences in astrocyte properties were abrogated in reeler and Dab1 conditional knockout mice, in which neuronal layers are disturbed, suggesting that neuronal layers are a prerequisite for the observed morphological and molecular differences of neocortical astrocytes. This study thus demonstrates the existence of layer-specific interactions between neurons and astrocytes, which may underlie their layer-specific functions. Several studies have suggested that astrocytes in the neocortex are more diverse than previously thought. Here, the authors describe layer-specific differences in morphology and molecular characteristics of astrocytes that depend on the neurons within those layers.

The majority of breast cancers are primarily hormone‐sensitive and can be managed by endocrine therapy, although therapy‐resistant or hormone‐refractory cancers need alternative treatments. Recently, increasing attention is being paid to RNA‐binding proteins (RBP) in cancer pathophysiology. The precise role of RBP in breast cancer, however, remains to be clarified. We herein show that an RBP non‐POU domain‐containing octamer binding (NONO) plays a critical role in the pathophysiology of breast cancers regardless of their hormone dependency. Clinicopathological and immunohistochemical study of 127 breast cancer cases showed that NONO is a significant independent prognostic factor for breast cancer patients. Notably, siRNA‐mediated NONO knockdown substantially repressed the proliferation of both hormone‐sensitive MCF‐7 and hormone‐refractory MB‐MDA‐231 breast cancer cells. Integrative analysis combined with expression microarray and RIP‐sequencing (RNA immunoprecipitation‐sequencing) showed that NONO post‐transcriptionally regulates the expression of cell proliferation‐related genes by binding to their mRNAs, as exemplified by S‐phase‐associated kinase 2 and E2F transcription factor 8. Overall, these results suggest that NONO is a key regulator for breast cancer proliferation through the pre‐mRNA splicing of cell proliferation‐related genes and could be a potential new diagnostic and therapeutic target for advanced disease. The present study shows that Drosophila behavior human splicing family RNA‐binding protein NONO plays a critical role in breast cancer tumorigenesis. Clinicopathological study defines that NONO immunoreactivity significantly correlates with poor overall and distant disease‐free survival of breast cancer patients. Cell‐based experiments show that NONO contributes to breast cancer proliferation by regulating SKP2 and E2F8 expression at the post‐transcriptional level. Our findings provide a new cancer strategy by applying NONO as a potential diagnostic and therapeutic target for breast cancer.

Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expression in unmeasured regions and tissues can enhance biologists’ histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE, and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC , an invasion marker, highlighted a small tumor-invasion region difficult to identify using raw spatial transcriptome data alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

N 6 -methyladenosine (m 6 A) is a modification that plays pivotal roles in RNA metabolism and function, although its functions in spliceosomal U6 snRNA remain unknown. To elucidate its role, we conduct a large-scale transcriptome analysis of a Schizosaccharomyces pombe strain lacking this modification and found a global change of pre-mRNA splicing. The most significantly impacted introns are enriched for adenosine at the fourth position pairing the m 6 A in U6 snRNA, and exon sequences weakly recognized by U5 snRNA. This suggests cooperative recognition of 5’ splice site by U6 and U5 snRNPs, and also a role of m 6 A facilitating efficient recognition of the splice sites weakly interacting with U5 snRNA, indicating that U6 snRNA m 6 A relaxes the 5’ exon constraint and allows protein sequence diversity along with explosively increasing number of introns over the course of eukaryotic evolution. Spliceosomal U6 snRNA is modified by m 6 A. Here the authors show that m 6 A modification on U6 snRNA promotes efficient recognition of the splice site through cooperating with U5 snRNA, indicating that U6 snRNA m 6 A allows the 5’ exon sequence variation.

In mice, transcription initiates at the mid-one-cell stage and transcriptional activity dramatically increases during the two-cell stage, a process called zygotic gene activation (ZGA). Associated with ZGA is a marked change in the pattern of gene expression that occurs after the second round of DNA replication. To distinguish ZGA before and after the second-round DNA replication, the former and latter are called minor and major ZGA, respectively. Although major ZGA are required for development beyond the two-cell stage, the function of minor ZGA is not well understood. Transiently inhibiting minor ZGA with 5, 6-dichloro-1-β-D-ribofuranosyl-benzimidazole (DRB) resulted in the majority of embryos arresting at the two-cell stage and retention of the H3K4me3 mark that normally decreases. After release from DRB, at which time major ZGA normally occurred, transcription initiated with characteristics of minor ZGA but not major ZGA, although degradation of maternal mRNA normally occurred. Thus, ZGA occurs sequentially starting with minor ZGA that is critical for the maternal-to-zygotic transition.

Despite the recent discovery of tissue regeneration enhancers in highly regenerative animals, upstream and downstream genetic programs connected by these enhancers still remain unclear. Here, we performed a genome-wide analysis of enhancers and associated genes in regenerating nephric tubules of Xenopus laevis. Putative enhancers were identified using assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) analyses. Their target genes were predicted based on their proximity to enhancers on genomic DNA and consistency of their transcriptome profiles to ATAC-seq/ChIP-seq profiles of the enhancers. Motif enrichment analysis identified the central role of Kr€uppel-like factors (Klf) in the enhancer. Klf15, a member of the Klf family, directly binds enhancers and stimulates expression of regenerative genes, including adrenoreceptor alpha 1A (adra1a), whereas inhibition of Klf15 activity results in failure of nephric tubule regeneration. Moreover, pharmacological inhibition of Adra1a-signaling suppresses nephric tubule regeneration, while its activation promotes nephric tubule regeneration and restores organ size. These results indicate that Klf15-dependent adrenergic receptor signaling through regeneration enhancers plays a central role in the genetic network for kidney regeneration.