Celldega is presented, an open-source Python and JavaScript library for scalable, interactive visualization and analysis of spatial-omics data that integrates custom analyses, performs neighborhood analysis, implements an efficient visualization-specific file format, and enables interactive exploration in notebooks and web galleries.
Abstract
Spatial-transcriptomics integrates high-dimensional single-cell data with microscopy to reveal cellular states, communication, and tissue organization. Analyzing this data requires a combination of multi-modal data processing, high-dimensional data analysis, spatial analysis, and integrated visualization. However, computational analysis is increasingly becoming a bottleneck as approaches mature and dataset sizes increase. Additionally, visualization can be challenging as open-source visualization tools struggle to scale to large datasets (exceeding 1 billion transcripts), and commercial visualization tools are costly, closed source, and inflexible. We present Celldega, an open-source Python and JavaScript library for scalable, interactive visualization and analysis of spatial-omics data. Celldega integrates custom analyses, performs neighborhood analysis, implements an efficient visualization-specific file format, and enables interactive exploration in notebooks and web galleries. We demonstrate Celldega across multiple technologies, tissues, and datasets, including 3D reconstructions of the developing whole mouse head comprising over four million cells. Finally, we demonstrate how Celldega can be utilized throughout the entire lifecycle of spatial data analysis, from quality control to building a public shareable gallery.
This protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation by emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints.
Hua-Lin Wang, Wei-Jia Chen, Yan Wu et al.· Journal of Visualized Experi...· 0 citations
Highly multiplexed immunofluorescence imaging visualizes and quantifies protein levels at single-cell resolution in intact tissues at low cost and high scalability. Analysis of these data involves multiple steps with many method and parameter choices that must be adapted to the data and analytical objectives. There is an unmet need for a toolbox that offers flexible end-to-end coverage of the workflow. Here we present ‘spatialproteomics’, a Python package that addresses these challenges. Spatialproteomics enables the processing and analysis of large imaging data, including steps such as segmentation, image processing and cell-type classification, while synchronizing shared coordinates across data modalities. We demonstrate spatialproteomics on images of reactive lymph nodes and B cell non-Hodgkin lymphomas from 132 patients. We showcase an end-to-end analysis from raw images to statistical characterization of how cell type composition and spatial distribution vary across indolent and aggressive lymphomas. Furthermore, we show how spatialproteomics can process Gigapixel whole-slide images. Spatialproteomics is a Python-based toolbox that supports end-to-end analysis of highly multiplexed imaging data.
Matthias Meyer-Bender, Harald Vöhringer, C. Schniederjohann et al.· Nature Methods· 1 citation
Abstract Summary Spatial transcriptomics (ST) data analysis and visualization face several challenges due to low sampling, diversity of tissue morphology and high drop-out inherent to the technique. New analysis methods are needed to overcome these challenges and promote continued biological discoveries. To overcome these constraints, we herein describe SpatialFlux, an R package developed to perform reference-based distance gradient analysis. SpatialFlux allows the identification and comprehensive visualization, in either an unbiased or biased manner, of differentially expressed genes and pathways across multiple ST tissues sections and along various axes, thus overcoming many inherent ST limitations and supporting continued biological discovery. Availability SpatialFlux package source code and vignette are freely available on Github (https://github.com/towerlab/SpatialFlux) and Zenodo (https://zenodo.org/records/21039284).
Dimitri Sokolowskei, Alexander J. Trostle, Achira B Shah et al.· Bioinform.· 0 citations
Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.
Nagomi Kurogi, Koki Shimbara, Tatsuya Koreeda et al.· PLoS Computational Biology· 0 citations
Recent advances in spatial transcriptomics have enabled the profiling of increasingly larger numbers of genes while retaining single-cell and subcellular resolution in situ. However, standardized bioinformatics workflows for analyzing these datasets have lagged behind, with existing pipelines focusing primarily on image processing and cell segmentation. To address this gap, we present nf_xpatial, a best-practices Nextflow pipeline for the downstream analysis of 10x Genomics Xenium data. The pipeline performs quality control, filtering, log and cell area normalization, multi-sample integration, and both expression-driven and spatially informed clustering across systematic parameter sweeps, allowing users to evaluate and compare clustering resolutions and spatial modeling parameters within a single reproducible run. Overall, nf_xpatial streamlines the processing of Xenium data from platform outputs to integrated single-cell and spatial clustering datasets, providing a standardized starting point from which biologists can finetune parameters and proceed to hypothesis-driven spatial analyses. Availability and implementation The source code and detailed documentation are freely available at https://github.com/U-BDS/nf_xpatial under the GPL-3 license. SUPPLEMENTARY INFORMATION Supplementary data is provided.
L. Potter, Austyn Trull, Nilesh Kumar et al.· bioRxiv· 0 citations
Understanding how different cell types assemble into tissues and organs, as well as how they interact to transmit and receive biological signals, is essential for advancing biomedical and biological research. Recent advancements in spatial transcriptomics (ST) technologies have opened new avenues for investigating biological systems by achieving subcellular spatial resolution. Since cells are the fundamental units of life, extracting single-cell information from high-resolution ST data is crucial. However, existing ST platforms often capture sparse transcript counts per spot or measure only a limited number of genes, complicating the extraction of comprehensive single-cell information. In this study, we introduce CellART, a unified framework designed to extract single-cell information across diverse high-resolution ST platforms, including VisiumHD, Xenium, MERFISH, and Stereo-seq. By leveraging multimodal data, such as staining images, spatial transcriptomics data, and single-cell RNA sequencing references, CellART simultaneously performs cell segmentation and cell type annotation through a seamless integration of deep learning and probabilistic modeling. We demonstrate the efficiency, generalizability, and robustness of CellART across various high-resolution spatial transcriptomics platforms, capable of processing datasets containing millions of spots. Comprehensive experiments validate the biological relevance and accuracy of the recovered cellular information within spatial configurations. Notably, we highlight the utility of CellART in breast and colorectal cancer datasets, showcasing its ability to fully leverage high-resolution ST data. By enhancing cellular resolution, CellART facilitates the identification of transient cancer cell states and immune cell subtypes. Furthermore, CellART enables investigations into cancer-immune cell communication, uncovering both established interactions and novel ligand-receptor pairs. The outputs of CellART are compatible with widely used community tools, facilitating a variety of downstream analyses.
Yu-Heng Chen, Yu-Yao Liu, Zhi-Wei Wang et al.· bioRxiv· 1 citation
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