Single-cell spatial transcriptomics dataset of basal-cell carcinoma (BCC), collected using in situ analysis platform 6k-plex CosMx SMI (Bruker Nanostring). The dataset is provided as a Seurat object in .rds format, and consists of in situ transcripts for 6075 genes in 232,802 cells + metadata (see Metadata section below).

Experimental conditions:

The dataset is derived from tumor sections of four patients. For 2 patients (A and B), nodular-ulcerated BCC samples (as determined by histopathological reports) and of mixed morphology (i.e. consisting of nodular and infiltrative areas). For 2 patients (C and D), we collected samples in three different conditions: i) from baseline (first, diagnostic biopsy); ii) 1 week later from the previously biopsied, wounded site; iii) 1 week after initial biopsy from a distant, unwounded site in the same tumor.

Data acquisition and annotation:

Tumor sections from four different patients were arranged on 2 slides. For each slide, 45 Fields-of-views (FOVs) (0.51mm by 0.51mm) were selected based on the immunofluorescent staining with morphological markers (DAPI, PanCK, CD45, CD68). Tissue slides were subjected to in situ chemistry and imaging using the CosMx SMI instrument. CosMx scan data were uploaded to Nanostring’s AtoMx spatial analysis platform, where pre-processing steps, imaging-barcode decoding and cell segmentation were performed. The pre-processed spatial transcriptomics data were exported to .rds files, and subsequent analyses were performed in R and Seurat.

To predict cell type identities from CosMx in situ transcripts, we applied the InSituType algorithm, using a set of 7 confidently-annotated scRNA-seq samples (Yerly et al. 2022 cohort) as a reference for label transfer, to which we manually added an average expression profile for neutrophils derived from Zilionis et al. (2019). Cells with < 100 detected transcripts and with >2% transcripts coming from negative control probes were labeled as “Low quality” cells. To define cancer cells that participate in homotypic or heterotypic interactions in our CosMx spatial datasets, we first calculated nearest neighbor graphs based on spatial coordinates using the BiocNeighbors package. We limited the neighbor search between cell centroids to a maximum distance D, corresponding to 2.5 times the average cell diameter in the CosMx dataset. If >90% of the cells within distance D of a given cancer cells were also cancer cells, the cancer cell was labeled as “homotypic”; otherwise it was labeled as “heterotypic”.

Metadata:

The dataset contains in situ transcripts for 6075 genes in 232,802 cells, as well as the following cell metadata:

• pat_fov: identifies unique tissue slide and FOV combinations. Note: Run identifier "Run6057_Patient1" includes samples from Patient A and Patient C, run identifier "Run6057_Patient4" includes samples from Patient B and Patient D. See Patient_ID metadata column.

• nCount_Nanostring: number of transcripts detected per cell

• nFeature_Nanostring: number of unique genes detected per cell

• cell Area, Aspect ratio, Height and Width

• mean and max readouts for a panel of antibodies (PanCK, CD68, membrane staining, CD45 and DAPI)

• Patient_ID, for four patients (A to D)

• Area_ID: identifying 8 areas of BCC tissue

• Condition: indicating the experimental condition of the sample

• Ulcerated_area: wheher the FOV was annotated in an ulcerated BCC area

• Dist_from_wound: for wounded samples, whether the FOV is located close or far from the wound

• Condition2: concatenates Condition (see above) with distance from wound

• Wound_direction: cardinal coordinates for the directionality of the wound with respect to the FOV

• Morphology: whether the FOV was annotated as nodular or infiltrative in terms of H&E morphology

• celltype: predicted cell type annotation based on inSituType label transfer

• celltype_prob: confidence score for cell type annotion

• Meta-programs signature scores for MP1 to MP7, calculated with UCell

• CAF signature scores (for wound-responding CAFs and baseline CAFs)

• Invasiveness score: corresponds to MP7 minus MP2 scores

• Invasive CAF score: corresponds to wound-responding CAF score minus baseline/unwounded CAF score

• homotypic: for cancer cells, whether cell-cell interactions are homotypic (only with other cancer cells) or heterotypic (with stromal components).

• X and Y coordinates of the centroid of each cell

• neighbors vector: lists the IDs of the nearest neighbors of each cell

• neighbors_encoding: the cell type distribution of the nearest neighbors; the vector is order according to the factor of cell types.

Here, we include the datasets required to replicate the analysis of single-cell RNAseq and CosMx spatial transcriptomics data associated with the manuscript "Spatial transcriptomic analyses of Ewing sarcoma reveals absence of human leukocyte antigen class I as a potential mechanism of primary resistance to immunotherapy". Code is include separately on github.

Metadata of the brain and extracranial melanoma metastases (Visium), and Merkel Cell Carcinoma (CosMx) samples used in the spatialGE analyses. The file also contains the accession numbers and links to the data used in this work.

This dataset contains the code and data used to perform the analysis, and produce the figures used in the accompanying SpaNorm manuscript that introduces the first and only spatially-aware library size normalisation method.

IntroductionThe advent of high throughput spatial transcriptomics (HST) has allowed for unprecedented characterization of spatially distinct cell communities within a tissue sample. While a wide range of computational tools exist for detecting cell communities in HST data, none allow for the characterization of community connectivity, i.e., the relative similarity of cells within and between found communities—an analysis task that can elucidate cellular dynamics in important settings such as the tumor microenvironment.MethodsTo address this gap, we introduce the analysis of community connectivity (ACC), which facilitates understanding of the relative similarity of cells within and between communities. We develop a Bayesian multi-layer network model called BANYAN for the integration of spatial and gene expression information to achieve ACC.ResultsWe demonstrate BANYAN's ability to recover community connectivity structure via a simulation study based on real sagittal mouse brain HST data. Next, we use BANYAN to implement ACC across a wide range of real data scenarios, including 10 × Visium data of melanoma brain metastases and invasive ductal carcinoma, and NanoString CosMx data of human-small-cell lung cancer, each of which reveals distinct cliques of interacting cell sub-populations. An R package banyan is available at https://github.com/dongjunchung/banyan.

Single-cell spatial transcriptomics and proteomics of APOE Christchurch in 5xFAD and PS19 mice

https://doi.org/10.5061/dryad.m63xsj4ck

Description of the data and file structure

We have submitted all processed RDS files (5xApoeCh_Protein_annotated_seurat.rds, PS19ApoeCh_Protein_annotated_seurat.rds, 5xApoeCh_RNA_annotated_seurat.rds, PS19ApoeCh_RNA_annotated_seurat.rds) analyzed using the R package Seurat. Sample metadata are stored in seurat@meta.data and organized in the same way for the 5xFAD and PS19 cohorts. For spatial proteomics, we have included the .csv files containing the parameters used to perform automated cell typing with the CELESTA algorithm (mouse_signature_matrix.csv, mouse_tuning_params.csv). Finally, we have submitted a .zip file containing the outputs from differential gene expression analysis (DGE_files.zip).

Files and variables

Single-cell spatial proteomics datasets

...

High-grade serous ovarian carcinoma (HGSOC) is a polyclonal disease characterised by the presence of subclones with distinct cancer genotypes. This intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and overall poor prognosis. For one patient sample we used NanoString CosMx Spatial Molecular Imaging (SMI) to analyse the organisation of tumour subclones and the cells that comprise their niche at single cell resolution. This dataset contains the related data and analysis scripts. Our study highlights the high degree of subclonal heterogeneity in HGSOC and that subclone-specific ligand and receptor expression patterns likely modulate how these tumour cells interact with their local microenvironment.

Melanoma clinical outcomes emerge from incompletely understood genetic mechanisms operating within the tumor and its microenvironment. Here, we utilized single-cell RNA-based spatial molecular imaging (RNA-SMI) in patient-derived archival tumors to reveal clinically relevant markers of malignancy progression and prognosis. We examined spatial gene expression of 203,472 cells inside benign and malignant melanocytic neoplasms, including melanocytic nevi, primary invasive and metastatic melanomas. Algorithmic cell clustering paired with intratumoral comparative 2D-analyses visualized synergistic, spatial gene signatures linking cellular proliferation, metabolism, and malignancy, validated by protein expression. Metastatic niches included upregulation of CDK2 and FABP5, which independently predicted poor clinical outcome in 473 melanoma patients via Cox regression analysis. More generally, our work demonstrates a framework for applying single-cell RNA-SMI technology toward identifying gene ..., NanoString® CosMx™ RNA Spatial Molecular Imaging (SMI) The protocol used for NanoString® CosMx™ RNA Spatial Molecular Imaging (SMI) was based on the method previously described by He et al. (High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol 40, 1794-1806. 2022). 5-μm formalin-fixed, paraffin-embedded (FFPE) tissue sections were mounted on VWR Superfrost Plus Micro slides (cat# 48311-703) and baked at 60°C overnight to improve tissue-slide adherence. The slides were prepared for in-situ hybridization (ISH) by heat-induced epitope retrieval (HIER) at 100°C for 15 min using ER1 epitope retrieval buffer (Leica Biosystems product, citrate-based, pH 6.0). Following HIER, the tissues were digested with 3 µg/ml Proteinase K diluted in ACD Protease Plus (Advanced Cell Diagnostics, Inc.) at 40°C for 30 minutes. Slides were washed twice with diethyl pyrocarbonate (DEPC)-treated water (DEPC H2O) and incubated in 0.0005% dilu..., , # Spatial Profiling of Benign and Malignant Melanocytic Tumors via RNA-SMI (CosMx)

https://doi.org/10.5061/dryad.ksn02v7b1

Description of the data and file structure

"Slide_1.zip", "Slide_2.zip", "Slide_3.zip", "Slide_4.zip":Â

Each .zip folder contains the RNA-SMI data pertaining to Slides 1-3, which examined 203,472 cells amongst ten melanocytic tumors (exact tumor identity per slide is outlined in fig. S1, with further slide information regarding microscopic field of view distribution outlined in fig. S2). Slide 4 contains the RNA-SMI data examining 84,312 cells including the nevus-melanoma mixed tumor featured in fig. 4 as well as four other melanocytic tumors (one melanoma, one cutaneous metastasis, and two nevi).

Specifically, each .zip folder contains the following files and folders:

Files:

Transcript file (tx_file.csv), which contains columns for:

• —fov (Field Of View (FOV) where transcript is located)

• —cell_ID (...

Additional file 2: Supplementary Tables. Table S1. Clinical information of the lung cancer patients and pathological profiles of the obtained tissues. Table S2. Estimated ligand-receptorinteraction in the PD-L1 high cancer tissuefrom the Visium dataset based on CellTalkDB or Omnipath databases. Table S3. Estimated ligand-receptorinteraction between tS2 and T lymphocytes from CosMx SMI dataset. Table S4. Differentially upregulated ligand-receptorinteraction in TNBC and ER+ breast cancer tissues from the Visium dataset