iSeq: An integrated tool to fetch public sequencing data.

MOTIVATION: High-throughput sequencing technologies (NGS) are increasingly used to address diverse biological questions. Despite the rich information in NGS data, particularly with the growing datasets from repositories like the GSA at NGDC, programmatic access to public sequencing data and metadata remains limited. RESULTS: We developed iSeq to enable quick and straightforward retrieval of metadata and NGS data from multiple databases via the command-line interface. iSeq supports simultaneous retrieval from GSA, SRA, ENA, and DDBJ databases. It handles over 25 different accession formats, supports Aspera downloads, parallel downloads, multi-threaded processes, FASTQ file merging, and integrity verification, simplifying data acquisition and enhancing the capacity for reanalyzing NGS data. AVAILABILITY: ISeq is freely available on Bioconda (https://anaconda.org/bioconda/iseq) and GitHub (https://github.com/BioOmics/iSeq). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data.

Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.

Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits.

Unraveling the regulatory mechanisms that govern complex traits is pivotal for advancing crop improvement. Here we present a comprehensive regulome atlas for rice (Oryza sativa), charting the chromatin accessibility across 23 distinct tissues from three representative varieties. Our study uncovers 117,176 unique open chromatin regions (OCRs), accounting for ~15% of the rice genome, a notably higher proportion compared to previous reports in plants. Integrating RNA-seq data from matched tissues, we confidently predict 59,075 OCR-to-gene links, with enhancers constituting 69.54% of these associations, including many known enhancer-to-gene links. Leveraging this resource, we re-evaluate genome-wide association study results and discover a previously unknown function of OsbZIP06 in seed germination, which we subsequently confirm through experimental validation. We optimize deep learning models to decode regulatory grammar, achieving robust modeling of tissue-specific chromatin accessibility. This approach allows to predict cross-variety regulatory dynamics from genomic sequences, shedding light on the genetic underpinnings of cis-regulatory divergence and morphological disparities between varieties. Overall, our study establishes a foundational resource for rice functional genomics and precision molecular breeding, providing valuable insights into regulatory mechanisms governing complex traits.

Reduction of relative intensity noise in a diamond Raman laser.

The relative intensity noise (RIN) characteristics of a continuous-wave diamond Raman laser are investigated for the first time. The results reveal the parasitic stimulated Brillouin scattering (SBS) that usually occurred with higher-order spatial modes in the diamond Raman resonator is a pivotal factor impacting the Raman longitudinal modes and deteriorating the RIN level. The diamond Raman laser automatically switches to single-longitudinal-mode operation and the RIN level is significantly decreased in the frequency range of 200 Hz to 1 MHz after the parasitic SBS is effectively suppressed through inserting a spatial aperture or a χ(2) nonlinear crystal into the cavity. Due to the introduction of additional nonlinear loss to the high intensity Raman fluctuations and the non-lasing spontaneous Raman modes, the χ(2) nonlinear crystal enables better performance in the RIN-level reduction compared to the spatial aperture which can only achieve SBS inhibition. The RIN reduction routes are well suited for various crystalline Raman media to achieve high power and low intensity noise laser at different wavelengths.

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification.

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors (TFs) in intricate regulatory networks in a cell-type specific manner. Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings. This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets, addressing batch effects and conserving biological variance. This integration spans a broad spectrum of tissues, including both below- and above-ground parts. Utilizing a rigorous approach for cell type annotation, we identified 47 distinct cell types or states, largely expanding our current view of plant cell compositions. We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression. Taken together, our study not only offers extensive plant cell atlas exploration that serves as a valuable resource, but also provides molecular insights into gene-regulatory programs that varies from different cell types.

Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat.

Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein-protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

Capture of regulatory factors via CRISPR-dCas9 for mechanistic analysis of fine-tuned SERRATE expression in Arabidopsis.

SERRATE (SE) plays an important role in many biological processes and under biotic stress resistance. However, little about the control of SE has been clarified. Here we present a method named native chromatin-associated proteome affinity by CRISPR-dCas9 (CASPA-dCas9) to holistically capture native regulators of the SE locus. Several key regulatory factors including PHYTOCHROME RAPIDLY REGULATED 2 (PAR2), WRKY DNA-binding protein 19 (WRKY19) and the MYB-family protein MYB27 of SE are identified. MYB27 recruits the long non-coding RNA-PRC2 (SEAIR-PRC2) complex for H3K27me3 deposition on exon 1 of SE and subsequently represses SE expression, while PAR2-MYB27 interaction inhibits both the binding of MYB27 on the SE promoter and the recruitment of SEAIR-PRC2 by MYB27. The interaction between PAR2 and MYB27 fine-tunes the SE expression level at different developmental stages. In addition, PAR2 and WRKY19 synergistically promote SE expression for pathogen resistance. Collectively, our results demonstrate an efficient method to capture key regulators of target genes and uncover the precise regulatory mechanism for SE.

scPlantDB: a comprehensive database for exploring cell types and markers of plant cell atlases.

Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled the comprehensive profiling of gene expression patterns at the single-cell level, offering unprecedented insights into cellular diversity and heterogeneity within plant tissues. In this study, we present a systematic approach to construct a plant single-cell database, scPlantDB, which is publicly available at https://biobigdata.nju.edu.cn/scplantdb. We integrated single-cell transcriptomic profiles from 67 high-quality datasets across 17 plant species, comprising approximately 2.5 million cells. The data underwent rigorous collection, manual curation, strict quality control and standardized processing from public databases. scPlantDB offers interactive visualization of gene expression at the single-cell level, facilitating the exploration of both single-dataset and multiple-dataset analyses. It enables systematic comparison and functional annotation of markers across diverse cell types and species while providing tools to identify and compare cell types based on these markers. In summary, scPlantDB serves as a comprehensive database for investigating cell types and markers within plant cell atlases. It is a valuable resource for the plant research community.

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis.

The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

A chromosome-level genome assembly provides insights into Cornus wilsoniana evolution, oil biosynthesis, and floral bud development.

Cornus wilsoniana W. is a woody oil plant with high oil content and strong hypolipidemic effects, making it a valuable species for medicinal, landscaping, and ecological purposes in China. To advance genetic research on this species, we employed PacBio together with Hi-C data to create a draft genome assembly for C. wilsoniana. Based on an 11-chromosome anchored chromosome-level assembly, the estimated genome size was determined to be 843.51 Mb. The N50 contig size and N50 scaffold size were calculated to be 4.49 and 78.00 Mb, respectively. Furthermore, 30 474 protein-coding genes were annotated. Comparative genomics analysis revealed that C. wilsoniana diverged from its closest species ~12.46 million years ago (Mya). Furthermore, the divergence between Cornaceae and Nyssaceae occurred >62.22 Mya. We also found evidence of whole-genome duplication events and whole-genome triplication γ, occurring at ~44.90 and 115.86 Mya. We further inferred the origins of chromosomes, which sheds light on the complex evolutionary history of the karyotype of C. wilsoniana. Through transcriptional and metabolic analysis, we identified two FAD2 homologous genes that may play a crucial role in controlling the oleic to linoleic acid ratio. We further investigated the correlation between metabolites and genes and identified 33 MADS-TF homologous genes that may affect flower morphology in C. wilsoniana. Overall, this study lays the groundwork for future research aimed at identifying the genetic basis of crucial traits in C. wilsoniana.

All-fiber laser system for all-optical 87Rb Bose Einstein condensate to space application.

In the development of the Cold Atom Physics Research Rack (CAPR) on board the Chinese Space Station, the laser system plays a critical role in preparing the all-optical 87 R b Bose-Einstein condensates (BECs). An all-fiber laser system has been developed for CAPR to provide the required optical fields for atom interaction and to maintain the beam pointing in long-term operation. The laser system integrates a 780 nm fiber laser system and an all-fiber optical control module for sub-Doppler cooling, as well as an all-fiber 1064 nm laser system for evaporative cooling. The high-power, single-frequency 780 nm lasers are achieved through rare-Earth doped fiber amplification, fiber frequency-doubling, and frequency stabilization technology. The all-fiber optical control module divides the output of the 780 nm laser system into 15 channels and regulates them for cooling, trapping, and probing atoms. Moreover, the power consistency of each pair of cooling beams is ensured by three power tracking modules, which is a prerequisite for maintaining stable MOT and molasses. A high-power, compact, controlled-flexible, and highly stable l064 nm all-fiber laser system employing two-stage ytterbium-doped fiber amplifier (YDFA) technology has been designed for evaporative cooling in the optical dipole trap (ODT). Finally, an all-optical 87 R b BEC is realized with this all-fiber laser system, which provides an alternative solution for trapping and manipulating ultra-cold atoms in challenging environmental conditions.

The ChIP-Hub Resource: Toward plantEncode.

Recent advances in sequencing technologies lead to the generation of an enormous amount of regulome and epigenome data in a variety of plant species. However, a comprehensive standardized resource is so far not available. In this chapter, we present ChIP-Hub, an integrative platform that has been developed based on the ENCODE standards by collecting and reanalyzing regulatory genomic datasets from 41 plant species. The ChIP-hub website is introduced in this chapter, including information on detailed steps of searching, data download, and online analyses, which facilitates users to explore ChIP-Hub. We also provide a cross-species comparison of chromatin accessibility information that gives a thorough view of evolutionary regulatory networks in plants.

Single cell transcriptomic analyses implicate an immunosuppressive tumor microenvironment in pancreatic cancer liver metastasis.

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease refractory to all targeted and immune therapies. However, our understanding of PDAC microenvironment especially the metastatic microenvironment is very limited partly due to the inaccessibility to metastatic tumor tissues. Here, we present the single-cell transcriptomic landscape of synchronously resected PDAC primary tumors and matched liver metastases. We perform comparative analysis on both cellular composition and functional phenotype between primary and metastatic tumors. Tumor cells exhibit distinct transcriptomic profile in liver metastasis with clearly defined evolutionary routes from cancer cells in primary tumor. We also identify specific subtypes of stromal and immune cells critical to the formation of the pro-tumor microenvironment in metastatic lesions, including RGS5+ cancer-associated fibroblasts, CCL18+ lipid-associated macrophages, S100A8+ neutrophils and FOXP3+ regulatory T cells. Cellular interactome analysis further reveals that the lack of tumor-immune cell interaction in metastatic tissues contributes to the formation of the immunosuppressive microenvironment. Our study provides a comprehensive characterization of the transcriptional landscape of PDAC liver metastasis.

A Linearly Polarized Wavelength-Tunable Q-Switched Fiber Laser with a Narrow Spectral Bandwidth of 112 MHz.

A tunable and narrow-bandwidth Q-switched ytterbium-doped fiber (YDF) laser is investigated in this paper. The non-pumped YDF acts as a saturable absorber and, together with a Sagnac loop mirror, provides a dynamic spectral-filtering grating to achieve a narrow-linewidth Q-switched output. By adjusting an etalon-based tunable fiber filter, a tunable wavelength from 1027 nm to 1033 nm is obtained. When the pump power is 1.75 W, the Q-switched laser pulses with a pulse energy of 10.45 nJ, and a repetition frequency of 11.98 kHz and spectral linewidth of 112 MHz are obtained. This work paves the way for the generation narrow-linewidth Q-switched lasers with tunable wavelengths in conventional ytterbium, erbium, and thulium fiber bands to address critical applications such as coherent detection, biomedicine, and nonlinear frequency conversion.

Efficient single-frequency 972†nm Yb-doped fiber amplifier with core pumping and elevated temperature.

In this work, we present a monolithic single-frequency, single-mode and polarization maintaining Yb-doped fiber (YDF) amplifier delivering up to 6.9 W at 972 nm with a high efficiency of 53.6%. Core pumping at 915 nm and elevated temperature of 300 °C were applied to suppress the unwanted 977 nm and 1030 nm ASE in YDF, so as to improve the 972 nm laser efficiency. In addition, the amplifier was further used to generate a single-frequency 486 nm blue laser with 590 mW of output power by single-pass frequency doubling.

scPlant: A versatile framework for single-cell transcriptomic data analysis in plants.

Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Asymmetric gene expression and cell-type-specific regulatory networks in the root of bread wheat revealed by single-cell multiomics analysis.

BACKGROUND: Homoeologs are defined as homologous genes resulting from allopolyploidy. Bread wheat, Triticum aestivum, is an allohexaploid species with many homoeologs. Homoeolog expression bias, referring to the relative contribution of homoeologs to the transcriptome, is critical for determining the traits that influence wheat growth and development. Asymmetric transcription of homoeologs has been so far investigated in a tissue or organ-specific manner, which could be misleading due to a mixture of cell types. RESULTS: Here, we perform single nuclei RNA sequencing and ATAC sequencing of wheat root to study the asymmetric gene transcription, reconstruct cell differentiation trajectories and cell-type-specific gene regulatory networks. We identify 22 cell types. We then reconstruct cell differentiation trajectories that suggest different origins between epidermis/cortex and endodermis, distinguishing bread wheat from Arabidopsis. We show that the ratio of asymmetrically transcribed triads varies greatly when analyzing at the single-cell level. Hub transcription factors determining cell type identity are also identified. In particular, we demonstrate that TaSPL14 participates in vasculature development by regulating the expression of BAM1. Combining single-cell transcription and chromatin accessibility data, we construct the pseudo-time regulatory network driving root hair differentiation. We find MYB3R4, REF6, HDG1, and GATAs as key regulators in this process. CONCLUSIONS: Our findings reveal the transcriptional landscape of root organization and asymmetric gene transcription at single-cell resolution in polyploid wheat.

An antisense intragenic lncRNA SEAIRa mediates transcriptional and epigenetic repression of SERRATE in Arabidopsis.

SERRATE (SE) is a core protein for microRNA (miRNA) biogenesis as well as for mRNA alternative splicing. Investigating the regulatory mechanism of SE expression is hence critical to understanding its detailed function in diverse biological processes. However, little about the control of SE expression has been clarified, especially through long noncoding RNA (lncRNA). Here, we identified an antisense intragenic lncRNA transcribed from the 3' end of SE, named SEAIRa. SEAIRa repressed SE expression, which in turn led to serrated leaves. SEAIRa recruited plant U-box proteins PUB25/26 with unreported RNA binding ability and a ubiquitin-like protein related to ubiquitin 1 (RUB1) for H2A monoubiquitination (H2Aub) at exon 11 of SE. In addition, PUB25/26 helped cleave SEAIRa and release the 5' domain fragment, which recruited the PRC2 complex for H3 lysine 27 trimethylation (H3K27me3) deposition at the first exon of SE. The distinct modifications of H2Aub and H3K27me3 at different sites of the SE locus cooperatively suppressed SE expression. Collectively, our results uncover an epigenetic mechanism mediated by the lncRNA SEAIRa that modulates SE expression, which is indispensable for plant growth and development.

The SMARCA4R1157W mutation facilitates chromatin remodeling and confers PRMT1/SMARCA4 inhibitors sensitivity in colorectal cancer.

Genomic studies have demonstrated a high frequency of genetic alterations in components of the SWI/SNF complex including the core subunit SMARCA4. However, the mechanisms of tumorigenesis driven by SMARCA4 mutations, particularly in colorectal cancer (CRC), remain largely unknown. In this study, we identified a specific, hotspot mutation in SMARCA4 (c. 3721C>T) which results in a conversion from arginine to tryptophan at residue 1157 (R1157W) in human CRC tissues associated with higher-grade tumors and controls CRC progression. Mechanistically, we found that the SMARCA4R1157W mutation facilitated its recruitment to PRMT1-mediated H4R3me2a (asymmetric dimethylation of Arg 3 in histone H4) and enhanced the ATPase activity of SWI/SNF complex to remodel chromatin in CRC cells. We further showed that the SMARCA4R1157W mutant reinforced the transcriptional expression of EGFR and TNS4 to promote the proliferation of CRC cells and patient-derived tumor organoids. Importantly, we demonstrated that SMARCA4R1157W CRC cells and mutant cell-derived xenografts were more sensitive to the combined inhibition of PRMT1 and SMARCA4 which act synergistically to suppress cell proliferation. Together, our findings show that SMARCA4-R1157W is a critical activating mutation, which accelerates CRC progression through facilitating chromatin recruitment and remodeling. Our results suggest a potential precision therapeutic strategy for the treatment of CRC patients carrying the SMARCA4R1157W mutation.