LnCeCell 2.0 is an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data.

We construct an updated database, LnCeCell 2.0, which is an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data.

LnCeCell, with the first version released in 2021, is a comprehensive database of cell-specific lncRNA-associated ceRNA networks and biomarkers at single-cell resolution. The LnCeCell database was first published on NAR in 2021. To date, the LnCeCell database has received 45 citations (Google Scholar) and more than 250,000 visits from 117 countries. The LnCeCell database is continuously updated to open a new door to personalised disease characterisation based on the principle of "One Cell, One Network, One World".

In recent years, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (stRNA-seq) have greatly expanded our understanding of complex microbial ecosystems and variant cell states with single-cell and spatial resolution in individual tissue sections. Within the tumour microenvironment, cells exhibit distinct cellular behaviours driven by the fine-tuning of gene expression and regulation. Thus, the identification of cell-specific gene regulatory networks will help us to understand the disease pathology of individual cells/spots and further contribute to precision medicine.

Fig1-1

Fig 1-1. The "One Cell, One Network, One World" concept of LnCeCell 2.0. Within the tumour microenvironment, cells exhibit distinct cellular behaviours driven by the fine-tuning of gene expression and regulatory networks.

We are pleased to introduce LnCeCell 2.0 ( http://bio-bigdata.hrbmu.edu.cn/LnCeCell ), an updated database with significantly expanded data and improved features, including:

  • New collection of 257 scRNA-seq and stRNA-seq datasets across 80 diseases/phenotypes with different clinical treatments (such as chemotherapy, immunotherapy and targeted therapy) and 80 human normal tissues;
  • Newly identified 836,581 cell-specific and spatial spot-specific lncRNA-associated ceRNA interactions and functional networks for 1,002,988 cells and 367,971 spatial spots;
  • Manually curated nearly 15,000 experimentally supported lncRNA biomarkers associated with cancer cell metastasis, recurrence, prognosis, circulation, drug resistance, immune response, etc;
  • Improved detail information of cell type and cell state annotation and subcellular locations of ceRNAs through manual curation from literature and related data sources;
  • Expanded curation of lncRNA/mRNA/miRNA expression profiles and follow-up clinical information of thousands of cancer patients.

LnCeCell 2.0 provides a user-friendly search and browse interface. In addition, as an important addition to the database, we have created a panel of flexible tools (including 8 comprehensive analysis tools and 16 mini analysis tools) to facilitate data retrieval and analysis. For example, the CeCellCluster tool explores gene expression, ceRNA abundance and their association with different cell populations, cell types, states, primary/metastatic sites, etc. The CeCellTraject tool illustrates the detailed ceRNA distribution of cell populations and explores the dynamic change of the ceRNA network along the cell developmental trajectory. The Multi-3D (scRNA-seq) and Multi-3D (stRNA-seq) tools perform multi-level crosstalk analysis between ceRNA networks and functional states that contribute to individual disease pathology and cell fate in an interactive 3D view. The CeCellLand tool provides an overview of ceRNA distributions across pan-cancers, normal tissues/organs and spatial tissue spots. In addition, each of the 16 mini-tools provides easy-to-use and fast analysis such as functional annotation, hallmark annotation, cell state annotation, cell clustering, survival analysis, correlation analysis, network construction, etc. Taken together, we expect that this updated database could facilitate the investigation of fine-tuned lncRNA-ceRNA networks with single-cell and spatial spot-resolution, and help us to understand the regulatory mechanisms behind complex microbial ecosystems.

LnCeCell 2.0 provides quick search and powerful analysis tools for high-throughput scRNA-seq & stRNA-seq datasets.

  • Navigation Bar The navigation bar provides quick access to all major sections of LnCeCell 2.0, including Home, Search, Browse, Comprehensive Tools, Mini Tools, Statistics, Download, and Help.
  • Search Button Click this button to open the search panel, allowing you to quickly search for specific genes, organs, diseases, and other relevant data within the LnCeCell 2.0 database.
  • Quick Access Buttons
    • Quick Search: Initiates a fast search process for ceRNA/organ/disease, etc.
    • Analysis Tools: Directs you to the comprehensive suite of analysis tools provided by LnCeCell 2.0.
    • LnCeCell 1.0: Provides access to the legacy version of the database for reference and comparative analysis.
  • Comprehensive Analysis Tools Explore a wide array of comprehensive analysis tools, such as:
    • CelCellCluster: Investigate gene expression, ceRNA abundance and their association with different cell populations, cell types, states, primary/metastatic sites, etc.
    • CelCellTraject: Illustrate the detailed ceRNA distribution of cell populations and explore the dynamic change of the ceRNA network along the cell developmental trajectory.
    • CelCellStateTraject: Explore causative interplay between ceRNA regulation and cell states along the cell developmental trajectory.
    • CelCellFuncTraject: Explore causative interplay between ceRNA regulation and functions (GO terms, pathways, hallmarks, etc.) along the cell developmental trajectory.
    • Multi-3D (scRNA-seq): Perform multi-level crosstalk analysis between ceRNA networks and functional states that contribute to individual disease pathology and cell fate in an interactive 3D view.
    • Multi-3D (stRNA-seq): Perform multi-level crosstalk analysis between ceRNA networks and functional states at spatial spot resolution in an interactive 3D view.
    • CeCellLand (scRNA-seq): An overview of ceRNA distribution across pan-cancer, normal tissues/organs at single-cell resolution.
    • CeCellLand (stRNA-seq): An overview of ceRNA distribution across pan-cancer, normal tissues/organs at spatial point resolution.
  • Mini Analysis Tools Access a variety of focused mini-tools for specialized analysis, including:
    • CellCluster: Investigate cell distribution of different clusters, cell types, states, primary/metastatic sites, etc.
    • CellTraject: Explore gene expression and detailed distribution of cell subpopulations along cell developmental trajectories.
    • CeCellExp: Explore gene expression and distribution of different cell clusters, cell types, states, primary/metastatic sites, etc.
    • CeHallmark: Identify ceRNA related cancer hallmarks.
    • CeFunction: Identify dysregulated functions of ceRNAs.
    • CeNet: Construct a ceRNA network for a candidate lncRNA/mRNA.
    • CeLocation: Visualise the subcellular locations of a ceRNA.
    • LncMarker: Identify lncRNA biomarkers supported by experimental validation.
    • Survival: Survival analysis of ceRNAs across more than 30 types of malignant cancers.
    • CellState: Explore dynamic changes in cell state along the pseudo-time course of cell development.
    • CellFunction: Explore dynamic change of cell functions along cell developmental pseudotime.
    • CeStateTalk: Investigate causal crosstalk between ceRNA and cell states.
    • CeFuncTalk: Investigate causal crosstalk between ceRNA and cell functions.
    • SpatialView: View the spatial map of corresponding sections in different spatial transcriptomic-seq data.
    • SpatialCe: View the spatial map of corresponding ceRNAs in different spatial sections.
    • SpatialFunction: View the spatial map of spot functions in different spatial sections.
  • New Features LnCeCell 2.0 offers enhanced features including:
    • 257 Datasets
    • 1.37M+ Cells
    • 86 Diseases
    • 836,561 ceRNAs
    • 127 Publications
  • Database Comparison Compare the data between LnCeCell 1.0 and LnCeCell 2.0
  • Our Works Explore various related projects and databases:
    • CellTracer: A comprehensive database to dissect the causative multilevel interplay contributing to cell development trajectories.
    • LncACTdb 3.0: A comprehensive database of experimentally supported ceRNA interactions and personalized networks contributing to precision medicine.
    • CellMarker 2.0: An updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data.
    • Lnc2Cancer 3.0: An updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data.
    • LincSNP 3.0: An updated database for linking functional variants to human long non-coding RNAs, circular RNAs and their regulatory elements.
    • LnCeVar: A comprehensive database of genomic variations that disturb ceRNA network regulation.
Fig2-1

Fig 2-1. LnCeCell's Home.

Investigate gene expression, ceRNA abundance and their association with different cell populations, cell types, states, primary/metastatic sites, etc.

  • Search Panel This panel allows users to specify the dataset and type of ceRNA to explore. Enter the mRNA, lncRNA, or ceRNA of interest in the value field and select the dataset from the dropdown menu to begin your search.
  • Results Table The results table displays information related to the search query, including related lncRNA, mRNA, ceRNA, and other relevant metrics. Users can click on the entries to explore detailed information about each result.
  • Basic Information Panel This panel provides comprehensive details about the selected dataset, including the dataset name, disease, organ, species, platform, number of cells, and patients. It also offers links to download the dataset and additional information.
  • Advanced Control Users can customize their analysis using advanced controls such as selecting different cell types, coordinate (e.g. UMAP, tSNE), point sizes, and axis display options. Click "Submit" to apply the chosen settings and update the visualization.
  • CellCluster By Class This figure shows the cell clustering map at different resolutions, allowing users to visualize how cells are grouped based on their characteristics.
  • CellCluster By Cell Type This figure shows the clustering of cells according to various types (e.g., state, sample, stage...), helping to identify the different cell types within the dataset.
  • lncRNA Expression This figure displays the expression levels of the selected lncRNA across the cell clusters.
  • mRNA Expression This figure displays the expression levels of the selected mRNA across the cell clusters.
  • ceRNA Expression This figure displays the expression levels of the selected ceRNA across the cell clusters.
  • lncRNA Mean Exp in Clusters This figure shows the boxplots of the average expression values of each category of the lncRNA at different clusters of the dataset, providing a comparative view of expression levels among clusters.
  • mRNA Mean Exp in Clusters This figure shows the boxplots of the average expression values of each category of the mRNA at different clusters of the dataset, providing a comparative view of expression levels among clusters.
  • ceRNA Mean Exp in Clusters This figure shows the boxplots of the average expression values of each category of the ceRNA at different clusters of the dataset, providing a comparative view of expression levels among clusters.
Fig2-1

Fig 3-1. CeCellCluster analysis interface.

Illustrate the detailed ceRNA distribution of cell populations and explore the dynamic change of the ceRNA network along the cell developmental trajectory.

  • Search Panel This panel allows users to specify the dataset and type of ceRNA to explore. Enter the mRNA, lncRNA, or ceRNA of interest in the value field and select the dataset from the dropdown menu to begin your search.
  • Results Table The results table displays information related to the search query, including related lncRNA, mRNA, ceRNA, and other relevant metrics. Users can click on the entries to explore detailed information about each result.
  • Basic Information Panel This panel provides comprehensive details about the selected dataset, including the dataset name, disease, organ, species, platform, number of cells, and patients. It also offers links to download the dataset and additional information.
  • Advanced Control Users can customize their analysis using advanced controls such as selecting different cell types, monocle (e.g., Monocle2, Monocle3), point sizes, and trackline display options. Click "Submit" to apply the chosen settings and update the visualization.
  • CellTraject By Class This figure shows the cell trajectory map by class, allowing users to visualize how cells progress along different developmental trajectories based on their characteristics.
  • CellTraject By Pseudotime This figure shows the cell trajectory map by pseudotime, helping to identify the temporal order of cell developmental states within the dataset.
  • lncRNA Expression This figure displays the expression levels of the selected lncRNA across the cell trajectories.
  • mRNA Expression This figure displays the expression levels of the selected mRNA across the cell trajectories.
  • ceRNA Expression This figure displays the expression levels of the selected ceRNA across the cell trajectories.
  • lncRNA Mean Exp in Clusters This figure shows the boxplots of the average expression values of each category of the lncRNA at different clusters of the dataset, providing a comparative view of expression levels among clusters.
  • mRNA Mean Exp in Clusters This figure shows the boxplots of the average expression values of each category of the mRNA at different clusters of the dataset, providing a comparative view of expression levels among clusters.
  • ceRNA Mean Exp in Clusters This figure shows the boxplots of the average expression values of each category of the ceRNA at different clusters of the dataset, providing a comparative view of expression levels among clusters.
Fig4-1

Fig 4-1. CeCellTraject analysis interface.

Explore causative interplay between ceRNA regulation and cell states along the cell developmental trajectory.

  • Search Panel This panel allows users to specify the dataset and type of ceRNA to explore. Enter the mRNA, lncRNA, or ceRNA of interest in the value field and select the dataset from the dropdown menu to begin your search.
  • Results Table The results table displays information related to the search query, including related lncRNA, mRNA, ceRNA, and other relevant metrics. Users can click on the entries to explore detailed information about each result.
  • Basic Information Panel This panel provides comprehensive details about the selected dataset, including the dataset name, disease, organ, species, platform, number of cells, and patients. It also offers links to download the dataset and additional information.
  • Advanced Control Users can customize their analysis using advanced controls such as selecting different cell states, coordinates (e.g., UMAP, tSNE), correlation methods, point sizes, and trackline display options. Click "Submit" to apply the chosen settings and update the visualization.
  • CellStateExp This figure shows the cell state expression map, allowing users to visualize how cell states are distributed along the developmental trajectory based on their characteristics.
  • StatePseuCor This figure displays the correlation between cell states and pseudotime, helping to identify the temporal progression of cell states within the dataset.
  • CellStateStateBoxPlot This figure shows boxplots of the normalized expression levels of cell states, providing a comparative view of expression levels among different states.
  • LncRNA Expression This figure displays the expression levels of the selected lncRNA across the cell trajectories.
  • mRNA Expression This figure displays the expression levels of the selected mRNA across the cell trajectories.
  • ceRNA Expression This figure displays the expression levels of the selected ceRNA across the cell trajectories.
  • lncRNA - Cell State Interplay This figure shows the interaction between the expression of the selected lncRNA and cell states, providing insight into their regulatory interplay.
  • mRNA - Cell State Interplay This figure shows the interaction between the expression of mRNA and cell states, providing insight into their regulatory interplay.
  • ceRNA - Cell State Interplay This figure shows the interaction between the expression of the selected ceRNA and cell states, providing insight into their regulatory interplay.
Fig5-1

Fig 5-1. CeCellState analysis interface.

Explore causative interplay between ceRNA regulation and functions (GO terms, pathways, hallmarks, etc.) along the cell developmental trajectory.

  • Search Panel This panel allows users to specify the dataset and type of ceRNA to explore. Enter the gene, lncRNA, or ceRNA of interest in the value field and select the dataset from the dropdown menu to begin your search.
  • Results Table The results table displays information related to the search query, including related lncRNA, gene, ceRNA, and other relevant metrics. Users can click on the entries to explore detailed information about each result.
  • Basic Information Panel This panel provides comprehensive details about the selected dataset, including the dataset name, disease, organ, species, platform, number of cells, and patients. It also offers links to download the dataset and additional information.
  • Advanced Control Users can customize their analysis using advanced controls such as selecting different cell states, correlation methods, point sizes, trackline display options, and functional pathways. Click "Submit" to apply the chosen settings and update the visualization.
  • FuncExp This figure shows the functional pathway expression map, allowing users to visualize how functional pathways are distributed along the developmental trajectory based on their characteristics.
  • FuncPseuCor This figure displays the correlation between functional pathways and pseudotime, helping to identify the temporal progression of functional pathways within the dataset.
  • FuncStateBoxPlot This figure shows boxplots of the normalized expression levels of functional states, providing a comparative view of expression levels among different states.
  • lncRNA Expression This figure displays the expression levels of the selected lncRNA across the cell trajectories.
  • mRNA Expression This figure displays the expression levels of the selected mRNA across the cell trajectories.
  • ceRNA Expression This figure displays the expression levels of the selected ceRNA across the cell trajectories.
  • lncRNA - Functional pathway Interplay This figure shows the interaction between the expression of the selected lncRNA and functional pathways, providing insight into their regulatory interplay.
  • mRNA - Functional pathway Interplay This figure shows the interaction between the expression of mRNA and functional pathways, providing insight into their regulatory interplay.
  • ceRNA - Functional pathway Interplay This figure shows the interaction between the expression of the selected ceRNA and functional pathways, providing insight into their regulatory interplay.
Fig6-1

Fig 6-1. CeCellFunc analysis interface.

Perform multi-level crosstalk analysis between ceRNA networks and functional states that contribute to individual disease pathology and cell fate in an interactive 3D view.

  • Search Panel This panel allows users to explore the available single-cell (SC) datasets within the LnCeCell2 database. Users can select a specific dataset by entering the dataset name, organ, or disease of interest in the search field. The table below the search field provides a summary of the datasets, including details such as disease name, number of patients, organ, number of cells, primary/metastatic status, treatment type, and platform used for sequencing.
  • Basic Information Panel This panel provides comprehensive details about the selected dataset, including the dataset name, disease, organ, species, platform, number of cells, and patients. It also offers links to download the dataset and additional information, allowing users to access all necessary data at a glance.
  • Advanced Control This section allows users to customize their multi-omics analysis by selecting specific types of ceRNAs, genes, cell states, and functional pathways. Users can input values for the selected ceRNA or gene and choose different cell states and functional pathways from dropdown menus. Additional parameters, such as line width, symbol type, visual map, and axis display options, can be configured to tailor the 3D visualization to their needs. Click "Submit" to apply the chosen settings and update the visualization.
  • Multi-3D (scRNA-seq) Visualization
    • 3D Interaction The interactive 3D visualization is a powerful tool that allows users to explore complex interactions within the dataset. It provides a spatial representation of how ceRNAs, genes, and various cell states and functional pathways interact. This feature helps in visualizing multi-level crosstalk and understanding the intricate relationships that contribute to disease pathology and cell fate. Users can rotate, zoom, and pan the 3D plot to gain different perspectives and detailed insights.
    • Color Coding The visualization uses a color map to represent the intensity of interactions. High and low interaction levels are displayed using different colors, making it easier to identify significant interactions at a glance. This color coding helps in quickly highlighting areas of interest and drawing attention to key patterns and anomalies within the data.
    • Axes Labels The axes are labeled with relevant parameters, such as pseudotime and monocle dimensions, which help in contextualizing the 3D plot. These labels provide important reference points that aid in understanding the trajectory and distribution of cells within the dataset. The axes labels also assist in correlating the 3D visualization with the underlying biological processes and functional states.
    • Parameter Adjustment Users can adjust various parameters to refine the visualization. This includes changing the line width, symbol type, and visual map settings to enhance the clarity and interpretability of the 3D plot. By customizing these parameters, users can tailor the visualization to better suit their specific analysis needs and preferences.
    • Functional Insights The 3D plot provides functional insights by mapping the interactions between ceRNAs, genes, cell states, and pathways. This holistic view helps in identifying critical points of regulation and interaction that drive disease progression and cellular responses. By examining these interactions in a 3D context, users can develop a deeper understanding of the biological mechanisms at play.
    • Data Integration The Multi-3D (scRNA-seq) tool integrates data from various omics layers, providing a comprehensive view of the biological system. This integration allows for a multi-faceted analysis that considers different molecular and cellular dimensions, thereby offering a more complete picture of the dataset. This feature is particularly useful for identifying cross-talk and interplay between different biological entities.
    • Interactive Exploration The interactive nature of the 3D visualization encourages exploration and discovery. Users can interact with the plot in real-time, making it possible to investigate specific regions and relationships in detail. This hands-on approach facilitates a more intuitive understanding of the data and supports hypothesis generation and testing.
    • Export and Share Users can export the 3D visualization and share it with collaborators. This feature ensures that findings can be easily disseminated and reviewed by others, fostering collaboration and further analysis. The export functionality supports various formats, making it convenient to include the 3D plots in presentations and publications.
Fig7-1

Fig 7-1. Multi-3D (scRNA-seq) analysis interface.

Perform multi-level crosstalk analysis between ceRNA networks and functional states at spatial spot resolution in an interactive 3D view.

  • Search Panel This panel allows users to explore the available spatial transcriptomics (ST) datasets within the LnCeCell2 database. Users can select a specific dataset by entering the dataset name, organ, or disease of interest in the search field. The table below the search field provides a summary of the datasets, including details such as disease name, number of patients, organ, number of cells, primary/metastatic status, treatment type, publication information, and the layer.
  • Basic Information Panel This panel provides comprehensive details about the selected dataset, including the dataset name, disease, organ, species, platform, number of cells, and patients. It also offers links to download the dataset and additional information, allowing users to access all necessary data at a glance.
  • Advanced Control This section allows users to customize their multi-omics analysis by selecting specific types of ceRNAs, genes, cell states, and functional pathways. Users can input values for the selected ceRNA or gene and choose different cell states and functional pathways from dropdown menus. Additional parameters, such as line width, symbol type, visual map, and axis display options, can be configured to tailor the 3D visualization to their needs. Click "Submit" to apply the chosen settings and update the visualization.
  • Multi-3D (scRNA-seq) Visualization
    • 3D Interaction The interactive 3D visualization is a powerful tool that allows users to explore complex interactions within the dataset. It provides a spatial representation of how ceRNAs, genes, and various cell states and functional pathways interact. This feature helps in visualizing multi-level crosstalk and understanding the intricate relationships that contribute to disease pathology and cell fate. Users can rotate, zoom, and pan the 3D plot to gain different perspectives and detailed insights.
    • Color Coding The visualization uses a color map to represent the intensity of interactions. High and low interaction levels are displayed using different colors, making it easier to identify significant interactions at a glance. This color coding helps in quickly highlighting areas of interest and drawing attention to key patterns and anomalies within the data.
    • Axes Labels The axes are labeled with relevant parameters, such as layers, rows, and columns, which help in contextualizing the 3D plot. These labels provide important reference points that aid in understanding the spatial distribution and organization of cells within the dataset. The axes labels also assist in correlating the 3D visualization with the underlying biological processes and functional states.
    • Parameter Adjustment Users can adjust various parameters to refine the visualization. This includes changing the line width, symbol type, and visual map settings to enhance the clarity and interpretability of the 3D plot. By customizing these parameters, users can tailor the visualization to better suit their specific analysis needs and preferences.
    • Functional Insights The 3D plot provides functional insights by mapping the interactions between ceRNAs, genes, cell states, and pathways. This holistic view helps in identifying critical points of regulation and interaction that drive disease progression and cellular responses. By examining these interactions in a 3D context, users can develop a deeper understanding of the biological mechanisms at play.
    • Data Integration The Multi-3D (stRNA-seq) tool integrates data from various omics layers, providing a comprehensive view of the biological system at a spatial resolution. This integration allows for a multi-faceted analysis that considers different molecular and cellular dimensions, thereby offering a more complete picture of the dataset. This feature is particularly useful for identifying cross-talk and interplay between different biological entities in a spatial context.
    • Interactive Exploration The interactive nature of the 3D visualization encourages exploration and discovery. Users can interact with the plot in real-time, making it possible to investigate specific regions and relationships in detail. This hands-on approach facilitates a more intuitive understanding of the data and supports hypothesis generation and testing.
    • Export and Share Users can export the 3D visualization and share it with collaborators. This feature ensures that findings can be easily disseminated and reviewed by others, fostering collaboration and further analysis. The export functionality supports various formats, making it convenient to include the 3D plots in presentations and publications.
Fig8-1

Fig 8-1. Multi-3D (stRNA-seq) analysis interface.

An overview of ceRNA distribution across pan-cancer, normal tissues/organs at single-cell resolution.

  • Search Panel This panel allows users to specify the dataset and type of ceRNA to explore within the LnCeCell2 scRNA-seq database. Enter the gene, lncRNA, or ceRNA of interest in the value field and select the dataset from the dropdown menu to begin your search. The panel provides a comprehensive overview of ceRNA distribution across various datasets, including pan-cancer and normal tissues/organs at single-cell resolution.
  • Results Table The results table displays information related to the search query, including related lncRNA, Gene, ceRNA, and other relevant metrics. Users can click on the entries to explore detailed information about each result, such as the lncRNA ENSG, Gene ENSG, and ceRNA count. The table also includes visual indicators for the number of cells and the log2 fold change in expression.
  • Basic Information Panel This panel provides comprehensive details about the selected dataset, including the dataset name, disease, organ, species, platform, number of cells, and patients. It also offers links to download the dataset and additional information, allowing users to access all necessary data at a glance.
  • CeRNA Distribution Overview (Normal)
    • This circular plot visualizes the distribution of ceRNAs across various normal tissues/organs. Each segment of the circle represents a different tissue or organ, with the inner circle highlighting the Jaccard index and the outer circle representing the percentage of the total ceRNA identified in that tissue/organ.
    • The plot provides a detailed overview of how ceRNAs are distributed across normal tissues, helping to identify patterns and relationships between different ceRNAs and their expression in normal biological contexts.
  • CeRNA Distribution Overview (Cancer)
    • This circular plot visualizes the distribution of ceRNAs across various cancer types. Each segment of the circle represents a different cancer type, with the inner circle highlighting the Jaccard index and the outer circle representing the percentage of the total ceRNA identified in that cancer type.
    • The plot provides a detailed overview of how ceRNAs are distributed across cancer types, helping to identify patterns and relationships between different ceRNAs and their expression in oncological contexts.
  • CeRNA Distribution Bar Chart (Normal) This bar chart provides a detailed view of the ceRNA distribution across normal tissues/organs. The chart includes the number of ceRNAs identified, the percentage of the total ceRNA identified, and the number of cells in which these ceRNAs are expressed. This visualization helps in understanding the prevalence and significance of different ceRNAs in normal biological contexts.
  • CeRNA Distribution Bar Chart (Cancer) This bar chart provides a detailed view of the ceRNA distribution across various cancer types. The chart includes the number of ceRNAs identified, the percentage of the total ceRNA identified, and the number of cells in which these ceRNAs are expressed. This visualization helps in understanding the prevalence and significance of different ceRNAs in oncological contexts.
Fig9-1

Fig 9-1. CeCellLand (scRNA-seq) analysis interface.

An overview of ceRNA distribution across pan-cancer, normal tissues/organs at spatial point resolution.

  • Search Panel This panel allows users to specify the dataset and type of ceRNA to explore within the LnCeCell2 stRNA-seq datasets. Enter the mRNA, lncRNA, or ceRNA of interest in the value field and select the dataset from the dropdown menu to begin your search. The panel provides a comprehensive overview of ceRNA distribution across various datasets, including pan-cancer and normal tissues/organs at spatial point resolution.
  • Results Table The results table displays information related to the search query, including related lncRNA, mRNA, ceRNA, and other relevant metrics. Users can click on the entries to explore detailed information about each result, such as pectage of ceRNA. The table also includes visual indicators for the number of cells and the log2 fold change in expression.
  • Basic Information Panel This panel provides comprehensive details about the selected dataset, including the dataset name, disease, organ, species, platform, number of cells, and patients. It also offers links to download the dataset and additional information, allowing users to access all necessary data at a glance.
  • CeRNA Distribution Overview (stRNA-seq)
    • This circular plot visualizes the distribution of ceRNAs across various normal tissues/organs at spatial point resolution. Each segment of the circle represents a different tissue or organ, with the inner circle highlighting the Jaccard index and the outer circle representing the percentage of the total ceRNA identified in that tissue/organ.
    • The plot provides a detailed overview of how ceRNAs are distributed across normal tissues at spatial resolution, helping to identify patterns and relationships between different ceRNAs and their expression in normal biological contexts.
  • CeRNA Distribution Bar Chart (stRNA-seq) This bar chart provides a detailed view of the ceRNA distribution across various cancer types at spatial point resolution. The chart includes the number of ceRNAs identified, the percentage of the total ceRNA identified, and the number of cells in which these ceRNAs are expressed. This visualization helps in understanding the prevalence and significance of different ceRNAs in oncological contexts.
Fig10-1

Fig 10-1. CeCellLand (stRNA-seq) analysis interface.

Investigate cell distribution of different clusters, cell types, states, primary/metastatic sites, etc.

Fig11-1

Fig 11-1. CellCluster.

Explore detailed distribution of cell subpopulations along cell developmental trajectories.

Fig12-1

Fig 12-1. CellTraject.

Explore ceRNA expression and distribution of different cell clusters, cell types, states, primary/metastatic sites, etc.

Fig13-1

Fig 13-1. CeCellExp.

Identify ceRNA related cancer hallmarks.

Fig14-1

Fig 14-1. CeHallmark.

Identify dysregulated functions of ceRNAs.

Fig15-1

Fig 15-1. CeFunction - Full functions.

Fig15-2

Fig 15-2. CeFunction - GeneOntology functions.

Fig15-3

Fig 15-3. CeFunction - Immune functions.

Construct a ceRNA network for a candidate lncRNA/mRNA.

Fig16-1

Fig 16-1. CeNet.

Visualise the subcellular locations of a ceRNA.

Fig17-1

Fig 17-1. CeLocation.

Identify lncRNA biomarkers supported by experimental validation.

Fig18-1

Fig 18-1. LncMarker.

Survival analysis of ceRNAs across more than 30 types of malignant cancers.

Fig19-1

Fig 19-1. Survival (mean).

Fig19-2

Fig 19-2. Survival (median).

Explore dynamic changes in cell state along the pseudo-time course of cell development.

Fig20-1

Fig 20-1. CellState.

Explore dynamic change of cell functions along cell developmental pseudotime.

Fig21-1

Fig 21-1. CellFunction.

Investigate causal crosstalk between ceRNA and cell states.

Fig22-1

Fig 22-1. CeStateTalk.

Investigate causal crosstalk between ceRNA and cell functions.

Fig23-1

Fig 23-1. CeFuncTalk.

View the spatial map of corresponding sections in different spatial transcriptomic-seq data.

Fig24-1

Fig 24-1. SpatialView.

View the spatial map of corresponding ceRNAs in different spatial sections.

Fig25-1

Fig 25-1. SpatialCe.

View the spatial map of spot functions in different spatial sections.

Fig26-1

Fig 26-1. SpatialFunction.

How to download dataset.

LnCeCell 2.0 is an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data. This platform offers a comprehensive collection of datasets, accessible through two main tables: the scRNA-seq dataset table with 204 datasets and the stRNA-seq dataset table with 53 datasets. Each entry in these tables includes extensive metadata, such as the dataset name, disease, species, accession number, treatment details, number of patients and cells, platform used, primary/metastatic status, cell type, and organ. Users can easily download the corresponding datasets by clicking on the colorful icons available for each entry, which supports multiple formats, including CSV, Excel, PDF, and more. This feature facilitates comprehensive data analysis and supports a wide range of research activities.

The scRNA-seq dataset table provides detailed information for each dataset, including identifiers, disease association, and the specific platform used for sequencing, among other details. The stRNA-seq dataset table offers similar comprehensive details, emphasizing the platform's commitment to providing high-quality spatial transcriptomics data. Both tables are designed to be interactive, allowing users to toggle column visibility, search for specific entries, and export data in various formats. These interactive features facilitate a more optimal user experience by offering flexible data exploration and straightforward integration into diverse analysis workflows.

The strength of LnCeCell 2.0 lies in its comprehensive collection of data and detailed metadata, which renders it a leading resource for researchers in the field. The platform's concentration on single-cell and spatial transcriptomics data aligns with the most recent developments and demands in biomedical research, offering cutting-edge tools and visualization options that empower researchers to derive meaningful insights. The platform's convenient data access and interactive exploration features underscore its utility as a robust tool for the scientific community. These features enable in-depth analysis and facilitate significant advancements in understanding lncRNA-associated ceRNA networks.

Fig27-1

Fig 27-1. Download data table (scRNA-seq).

Fig27-2

Fig 27-2. Download data table (stRNA-seq).