Somatic variant analysis suite: copy number variation clonal visualization online platform for large-scale single-cell genomics.

The recent advance of single-cell copy number variation (CNV) analysis plays an essential role in addressing intratumor heterogeneity, identifying tumor subgroups and restoring tumor-evolving trajectories at single-cell scale. Informative visualization of copy number analysis results boosts productive scientific exploration, validation and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, single-cell Somatic Variant Analysis Suite (scSVAS), for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell genomic analysis that provides an arsenal of unique functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may conduct scientific discoveries, share interactive visualizations and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing and publishing single-cell CNV profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

CLARIFY: cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics.

MOTIVATION: Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model. RESULTS: We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks. AVAILABILITY AND IMPLEMENTATION: The source code and data is available at https://github.com/MihirBafna/CLARIFY.

TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history.

Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed.

Diastereoselective Synthesis of Dihydrobenzofuran-Fused Spiroindolizidines via Double-Dearomative [3 + 2] Cycloadditions.

Spiroindolizidine oxindoles represent a kind of privileged scaffold in many biologically active natural alkaloids. 2,3-Dihydrobenzofuran derivatives exhibit significant bioactivities in a variety of pharmaceuticals. Herein, we assembled these two privileged fragments into a small molecule via double-dearomative [3 + 2] cycloadditions with pyridinium ylides and 2-nitrobenzofurans. This protocol features remarkable advantages including wide substrate scope, mild condition, high level of diastereoselectivities and yields. Thus, a collection of spiroindolizidine-fused dihydrobenzofurans/indolines were facilely produced efficiently.

Outstanding Electrochemical Performance of Ni-Rich Concentration-Gradient Cathode Material LiNi0.9Co0.083Mn0.017O2 for Lithium-Ion Batteries.

The full-concentrationgradient LiNi0.9Co0.083Mn0.017O2 (CG-LNCM), consisting of core Ni-rich LiNi0.93Co0.07O2, transition zone LiNi1-x-yCoxMnyO2, and outmost shell LiNi1/3Co1/3Mn1/3O2 was prepared by a facile co-precipitation method and high-temperature calcination. CG-LNCM was then investigated with an X-ray diffractometer, ascanning electron microscope, a transmission electron microscope, and electrochemical measurements. The results demonstrate that CG-LNCM has a lower cation mixing of Li+ and Ni2+ and larger Li+ diffusion coefficients than concentration-constant LiNi0.9Co0.083Mn0.017O2 (CC-LNCM). CG-LNCM presents a higher capacity and a better rate of capability and cyclability than CC-LNCM. CG-LNCM and CC-LNCM show initial discharge capacities of 221.2 and 212.5 mAh g-1 at 0.2C (40 mA g-1) with corresponding residual discharge capacities of 177.3 and 156.1 mAh g-1 after 80 cycles, respectively. Even at high current rates of 2C and 5C, CG-LNCM exhibits high discharge capacities of 165.1 and 149.1 mAh g-1 after 100 cycles, respectively, while the residual discharge capacities of CC-LNCM are as low as 148.8 and 117.9 mAh g-1 at 2C and 5C after 100 cycles, respectively. The significantly improved electrochemical performance of CG-LNCM is attributed to its concentration-gradient microstructure and the composition distribution of concentration-gradient LiNi0.9Co0.083Mn0.017O2. The special concentration-gradient design and the facile synthesis are favorable for massive manufacturing of high-performance Ni-rich ternary cathode materials for lithium-ion batteries.

Oviz-Bio: a web-based platform for interactive cancer genomics data visualization.

Genetics data visualization plays an important role in the sharing of knowledge from cancer genome research. Many types of visualization are widely used, most of which are static and require sufficient coding experience to create. Here, we present Oviz-Bio, a web-based platform that provides interactive and real-time visualizations of cancer genomics data. Researchers can interactively explore visual outputs and export high-quality diagrams. Oviz-Bio supports a diverse range of visualizations on common cancer mutation types, including annotation and signatures of small scale mutations, haplotype view and focal clusters of copy number variations, split-reads alignment and heatmap view of structural variations, transcript junction of fusion genes and genomic hotspot of oncovirus integrations. Furthermore, Oviz-Bio allows landscape view to investigate multi-layered data in samples cohort. All Oviz-Bio visual applications are freely available at https://bio.oviz.org/.

MIRIA: a webserver for statistical, visual and meta-analysis of RNA editing data in mammals.

BACKGROUND: Adenosine-to-inosine RNA editing can markedly diversify the transcriptome, leading to a variety of critical molecular and biological processes in mammals. Over the past several years, researchers have developed several new pipelines and software packages to identify RNA editing sites with a focus on downstream statistical analysis and functional interpretation. RESULTS: Here, we developed a user-friendly public webserver named MIRIA that integrates statistics and visualization techniques to facilitate the comprehensive analysis of RNA editing sites data identified by the pipelines and software packages. MIRIA is unique in that provides several analytical functions, including RNA editing type statistics, genomic feature annotations, editing level statistics, genome-wide distribution of RNA editing sites, tissue-specific analysis and conservation analysis. We collected high-throughput RNA sequencing (RNA-seq) data from eight tissues across seven species as the experimental data for MIRIA and constructed an example result page. CONCLUSION: MIRIA provides both visualization and analysis of mammal RNA editing data for experimental biologists who are interested in revealing the functions of RNA editing sites. MIRIA is freely available at https://mammal.deepomics.org.

Over-expression of a poor prognostic marker in prostate cancer: AQP5 promotes cells growth and local invasion.

BACKGROUND: The aquaporins (AQPs), water channel proteins, are known playing a major role in transcellular and transepithelial water movement; they also exhibit several properties related to tumor development. The aim of the present study is to elucidate whether the expression of AQP5 is a strong prognostic biomarker for prostate cancer, and the potential role in the progression of prostate cancer cells. METHODS: AQP5 expression was measured in 60 prostate cancer tissues and cells (both PC-3 and LNCaP) by immunohistochemistry and immunofluorescence assay. AQP5 gene amplification was detected with FISH (fluorescence in situ hybridization). Proliferation and migration of cells and AQP5 siRNA cells were detected with MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) and Boyden chambers. Circulating tumor cells (CTCs) were detected by imFISH staining (CEP8-CD45-DAPI) assay. RESULTS: The results showed that in 60 tumor specimens, 19 (31.7%) patients showed high level of AQP5 expression, while 30 (50.0%) showed a moderate, intermediate level of staining, and 11 (18.3%) showed an absence of AQP5 staining, respectively. High-expression of AQP5 protein frequently accompanied gene amplification detection with FISH. The AQP5 over-expression was also associated with TNM stage (P=0.042), and lymph node metastasis (P=0.001). The relationships between age or tumor size with the expression of AQP5 were not significant (P>0.05). A positive correlation between the number of CTCs and AQP5 expression (P<0.05) was demonstrated. In addition, patients who were negative for AQP5 had superior cumulative survival rate than those who were positive for it. Over-expression of AQP5 protein was also found in prostate cancer cells and cell proliferation and migration were significantly attenuated by AQP5-siRNA. CONCLUSIONS: We concluded that AQP5 in prostate cancer was an independent prognostic indicator. AQP5 over-expression was likely to play a role in cell growth and metastasis. These conclusions suggest that AQP5 may be an effective therapeutic target for prostate cancer.

LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data.

Understanding how single cells divide and differentiate into different cell types in developed organs is one of the major tasks of developmental and stem cell biology. Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for the reconstruction of the cell division tree, and even the detection of cell types and differentiation trajectories at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expression data are emerging, aiming to improve the accuracy of lineage reconstruction. However, effectively incorporating the gene expression data requires a reasonable model on how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), a method that integrates the lineage barcode and gene expression data using the asymmetric cell division model and infers cell lineage under a framework combining Neighbor Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. Moreover, LinRace can output the cell states (cell types) of ancestral cells, which is rarely performed with existing lineage reconstruction methods. The information on ancestral cells can be used to analyze how a progenitor cell generates a large population of cells with various functionalities. LinRace is available at: https://github.com/ZhangLabGT/LinRace.

LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data.

Understanding how single cells divide and differentiate into different cell types in developed organs is one of the major tasks of developmental and stem cell biology. Recently, lineage tracing technology using CRISPR/Cas9 genome editing have enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for the reconstruction of the cell division tree, and even the detection of cell types and differentiation trajectories at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expression data are emerging, aiming to improve the accuracy of lineage reconstruction. However, effectively incorporating the gene expression data requires a reasonable model on how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), a method that integrates the lineage barcode and gene expression data using the asymmetric cell division model and infers cell lineage under a framework combining Neighbor Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods for lineage reconstruction. Moreover, LinRace can output the cell states (cell types) of ancestral cells, which is rarely performed with existing lineage reconstruction methods. The information on ancestral cells can be used to analyze how a progenitor cell generates a large population of cells with various functionalities. LinRace is available at: https://github.com/ZhangLabGT/LinRace.

LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data.

Lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for inference of cell lineage and cell types at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expressions are emerging. Effectively incorporating the gene expression data requires a reasonable model of how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), which integrates lineage barcode and gene expression data using asymmetric cell division model and infers cell lineages and ancestral cell states using Neighbor-Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. With inferred ancestral states, LinRace can also show how a progenitor cell generates a large population of cells with various functionalities.

scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks.

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, while also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including cell clustering and trajectory inference, multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

scMultiSim: simulation of single cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions.

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, hile also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data, many of them were not benchmarked before due to the lack of proper tools. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

Unintended changes in ocular biometric parameters during a 6-month follow-up period after FS-LASIK and SMILE.

BACKGROUND: Corneal refractive surgery has become reliable for correcting refractive errors, but it can induce unintended ocular changes that alter refractive outcomes. This study is to evaluate the unintended changes in ocular biometric parameters over a 6-month follow-up period after femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK) and small incision lenticule extraction (SMILE). METHODS: 156 consecutive myopic patients scheduled for FS-LASIK and SMILE were included in this study. Central corneal thickness (CCT), mean curvature of the corneal posterior surface (Kpm), internal anterior chamber depth (IACD) and the length from corneal endothelium to retina (ER) were evaluated before and after surgery over a 6-month period. RESULTS: Both the FS-LASIK and SMILE groups (closely matched at the pre-surgery stage) experienced flatter Kpm, shallower IACD and decreased ER 1 week post-surgery (P < 0.01), and these changes were larger in FS-LASIK than in SMILE group. During the 1 week to 6 months follow up period, Kpm, IACD and ER remained stable unlike CCT which increased significantly (P < 0.05), more in the FS-LASIK group. CONCLUSIONS: During the follow up, the posterior corneal surface became flatter and shifted posteriorly, the anterior chamber depth and the length from the corneal endothelium to retina decreased significantly compared with the pre-surgery stage. These unintended changes in ocular biometric parameters were greater in patients undergoing FS-LASIK than SMILE. The changes present clear challenges for IOL power calculations and should be considered to avoid affecting the outcome of cataract surgery.

Effectiveness of the Goldmann Applanation Tonometer, the Dynamic Contour Tonometer, the Ocular Response Analyzer and the Corvis ST in Measuring Intraocular Pressure following FS-LASIK.

Purpose: To test the performance of the four tonometers in providing IOP measurements that were free of the effects of corneal biomechanics changes caused by refractive surgery.Methods: Four tonometers were employed to provide IOP measurements for 65 participants who accepted Femtosecond laser-assisted LASIK (FS-LASIK). The measurements included GAT-IOP by the Goldmann Applanation Tonometer, DCT-IOP by the Dynamic Contour Tonometer, Goldmann-correlated IOP (ORA-IOPg) and corneal-compensated IOP (ORA-IOPcc) by the Ocular Response Analyzer, and uncorrected IOP (CVS-IOP) and biomechanically corrected IOP (CVS-bIOP) by the Corvis ST. Statistical analyses were performed to assess the association of the differences in IOP caused by FS-LASIK with central corneal thickness (CCT), mean corneal curvature (Km), age, refractive error correction (REC), optical zone diameter (OZD), ablation zone diameter (AZD), residual stromal bed thickness (RSB) and RSB ratio (RSB/CCT). Multiple linear regression models were constructed to explore factors influencing IOP changes.Results: All four tonometers exhibited significant differences between IOP measurements taken pre and post-surgery except for CVS-bIOP in the low to moderate myopia group (t = 1.602, p = .12). CVS-bIOP, followed by DCT-IOP, provided the best agreement between pre and post-FS-LASIK measurements with the lowest differences in IOP and the narrowest limits of agreement. The pre-post IOP differences were also significantly associated with the reduction in CCT in only GAT-IOP, ORA-IOPg, and CVS-IOP. CVS-bIOP and ORA-IOPcc were the only measurements that were not correlated with CCT, Km or age both before and after FS-LASIK.Conclusions: The biomechanically corrected bIOP from the Corvis ST provided post-FS-LASIK measurements that were in closest agreement with those obtained before surgery. In comparison, GAT-IOP, ORA-IOPg, ORA-IOPcc, and CVS-IOP appeared to be more influenced by the changes in corneal biomechanics caused by FS-LASIK.

miR-326 functions as a tumor suppressor in human prostatic carcinoma by targeting Mucin1.

Accumulating evidence suggests that microRNA-326 (miR-326) serves as a tumor suppressor in the initiation and progression of several human malignancies. However, the biological function and underlying molecular mechanism of miR-326 in prostatic carcinoma (PCa) remains largely unknown. In the present study, we found that miR-326 expression level was significantly downregulated in both primary PCa and castration-resistant PCa (CRPC) tissue samples as detected by qRT-PCR. Downregulation of miR-326 was closely associated with aggressive progression and poor prognosis of primary PCa patients. Gain- and lose- functional experiments revealed that forced expression of miR-326 significantly inhibited cell proliferation, colony formation, migration and invasion, induced G0/G1 cell cycle arrest, and promoted apoptosis in PCa cells in vitro, whereas, knockdown of miR-326 expression showed the opposite results. Overexpression of miR-326 also suppressed tumor growth in xenografted nude mice in vivo. Moreover, Luciferase reporter, qRT-PCR, and western blot assays identified that the 3'-untranslated region (3'-UTR) of Mucin1 (MUC1) was a direct target region of miR-326. Spearman's correlation analysis also confirmed an inverse relationship between miR-326 and MUC1 expressions in primary PCa tissue samples. In addition, restoration of MUC1 expression effectively abrogated the inhibitory effects of miR-326 on PCa proliferation, invasion and migration through the activation of JNK signaling pathway. Therefore, these data indicated that miR-326 functioned as a tumor suppressor in PCa by negatively regulating MUC1, and that miR-326 might serve as a potential therapeutic candidate for PCa treatment.