Journal: BMB Reports

Article Title: Recent advances in spatially resolved transcriptomics: challenges and opportunities

doi: 10.5483/BMBRep.2022.55.3.014

Figure Lengend Snippet: Integration of spatially resolved transcriptomic data with other methods

Article Snippet: Robust cell type decomposition (RCTD) is based on statistical model maximum-likelihood estimation to approximate the proportions of spatially localized cellular subtypes in spatially resolved transcriptomic data such as Slide-seq or 10X Genomics Visium datasets ( ).

Techniques: Derivative Assay, In Situ Hybridization, Expressing, Microarray, Marker, Modification, Staining, In Silico, Hybridization, Sequencing, Imaging, Mass Spectrometry

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Schematic overview of the generative process used to produce artificial spatial data. 1) First a set of seeding cells (red and blue circles) are placed in a defined tissue domain (square), every seeding cell hosts one unique copy number event. 2) The cells are allowed to “grow” within the tissue domain until the number of cells in the domain exceeds a predetermined number. 3) Mutations in the genome occur stochastically during growth and as a result, subpopulations (indicated by colour) of cells with similar genomic profiles arise. 4) Unoccupied space in the tissue domain is filled with benign cells (no copy number variations), spatial capture locations are placed in a grid over the grown tissue and transcripts are “captured” from the cells overlying each spot. 5) Synthetic spatial expression data is produced together with associated ground truth genomic data (both on spot and cell level). b , Results from applying siCNV (bottom) to a set of synthetic data together with ground truth information (top), only cells residing at spots being annotated as non-benign are shown. Blue indicates a deletion event while red indicates an amplification event. The ground truth shows the genomic profiles for all cells contributing to the spots assigned to a given clone. Comparing the inferred state with the ground truth on a clone 19 level, the average accuracy across genes was 0.90 (standard deviation 0.10) c , Spatial organization of the synthetic data analysed in (b), with thumbnail of the complete cell population in the artificial tissue, each pixel corresponding to a cell. The cells’ intensity levels are proportional to their total number of associated copy number events. Circles represent the spots used to “capture” transcripts. Spots are coloured by their inferred clone identity. Note how Clone 2, predicted to have zero copy number events, is found along the borders of both foci, where there’s a mixture of benign and non-benign cells. d , siCNV outputs from simulated synthetic data of spots simulating ST 1k array (low-resolution) with 100 μm spot diameter and centre-to-centre distance of 200 μm. e , Visium (high-resolution). High resolution spots were 0.55x size of low resolution and had 5x more spots per area. The synthetic ground truth data were identical for both.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Expressing, Produced, Amplification, Standard Deviation

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , UMAP summary of GEFs from 1k spatial transcriptomics experiments of prostate samples from patient 1. b , UMAP summary of GEFs from high resolution Visium experiments of prostate samples from patient 1. Top marker genes for each GEF are available in Supplementary Table , . c , Benign GEFs from b (high resolution) were used as a reference set for analysis of d , Tumour GEFS from b (high resolution). e , Snapshot of inferCNV profiles for chr 7 and 8 from GEF10. GEF inferCNV heterogeneity is highlighted by 3 subclones: the first harbouring no changes to chr 7 and 8, the second having a deletion and amplification in chr 8, and the last having alterations in both chr 7 and chr 8. While further subclustering of GEF10 spots using gene expression factors improved GEF to clone concordance, GEF to clone heterogeneity remained. f , Tumor GEFs distribution by siCNV clones (Fig. ). GEF = Gene Expression Factor, chr = Chromosome, siCNV = spatial inferCNV.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Marker, Amplification, Gene Expression, Clone Assay

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Visual selection of benign epithelial spots harbouring the least amount of inferred copy number variations, as outlined by the black box bounding box. Arrows identify dendrogram nodes corresponding to barcoded spots within the box. b , InferCNV output of the dendrogram nodes with numerical identifiers for selection corresponding to Panel a. c , Finalized benign reference set from analysis of epithelial cells in prostate patient 1, section H2_1 (Fig. ). d , Global spatial inferCNV profiles of the selected benign reference set from panel a, the remainder of the benign not included in the reference set, altered benign (Clone C, Fig. ), and the other Visium spots with luminal epithelial annotations (PIN, GG1, GG2, GG4, GG4 Cribriform).

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Selection

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Genome-wide derived analysis (siCNVs) for all Visium spots harbouring tumour from prostate patient 1. Clonal groupings of spots (with approximately 10–15 cells each) were determined by hierarchical clustering. Chr., chromosome. b , Phylogenetic clone tree of the tumour clones from a , with grey clones representing unobserved, inferred common ancestors. Clone circle area is proportional to the number of spots and branch length was determined by weighted quantity of CNVs (both on a logarithmic scale). siCNV changes for each clone are available in Supplementary Table . c , Representation of all tissue sections from prostate patient 1. Thicker black lines denote original boundaries annotated by initial clinical pathology. d , Consensus epithelial histological annotations for sections H1_4, H1_5 and H2_5, corresponding to the right tumour focus. e , Spatial visualization of tumour clones (from a ). The dashed lines mark areas where no spatial transcriptomics data were obtained owing to these regions being outside of barcoded array surfaces.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Genome Wide, Derivative Assay, Clone Assay

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Consensus pathology annotations for tumour spots from sections H2_1, H2_2, and H1_2. b , Clonal groupings of spots (approx. 10-15 cells each) determined by hierarchical clustering. c , Distinct siCNV profile of GG1 tumour focus from organscale prostate patient 1. siCNV profiling of epithelial Visium spots from section H1_2. d , Spot level histology and siCNV clone calls. GG = ISUP Gleason ‘Grade Group’, siCNV = spatial inferCNV.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques:

Journal: Nature

Article Title: Spatially resolved clonal copy number alterations in benign and malignant tissue

doi: 10.1038/s41586-022-05023-2

Figure Lengend Snippet: a , Somatic WGS CNV profile of patient 1 diagnosed with medulloblastoma (grade IV, desmoplastic/nodular, SHH-activated) with b , match normal blood. c , Somatic WGS CNV profile of Chr 2, 3 and 9 of patient 2 diagnosed with medulloblastoma (grade IV, classic morphology, SHH-activated) with d , match normal blood. Notably inferCNV analysis on Visium data did not show any genomic variability in chr 2 but since Visium and WGS data were generated from different locations of each tumour, we speculate that the observed WGS CNV patterns in patient 2 could be due to the inherent spatial heterogeneity of DNA copy number alterations observed by others when sampling multiple sites of medulloblastoma tumours. e , Somatic WGS CNV profile of Chr 2, 3 and 9 of patient 3 diagnosed with CNS embryonal tumour (grade IV, multi-layered rosettes, NOS) with d , match normal blood. No CNV was detected by WGS in the chromosomes not displayed. WGS = Whole-genome sequencing. Chr = Chromosome. SHH = Sonic hedgehog. CNS = Central nervous system. NOS = Not otherwise specified.

Article Snippet: For 10x Visium arrays, specifics regarding data processing before data analysis after demultiplexing of FASTQ files have been described elsewhere for the human SCC specimen and datasets provided by 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

Techniques: Generated, Sampling, Sequencing

Journal: NAR Cancer

Article Title: Spatial transcriptomics in breast cancer reveals tumour microenvironment-driven drug responses and clonal therapeutic heterogeneity

doi: 10.1093/narcan/zcae046

Figure Lengend Snippet: Tumour and TME dissection in ST breast cancer samples. ( A ) Schematic representation of the analysis workflow followed to label each spot as either tumour or TME. ( B ) Venn diagram representing the overlapping between the four annotation sources used to label tumour spots. An example tissue slide containing three regions coloured by ( C ) pathologist annotations, ( D ) scaled ESTIMATE score, which is inversely proportional to the tumour purity, ( E ) cancer cell proportions and ( F ) clonal structure of the tumour. ( G ) Final tumour and TME labelling based on all previous annotations, as summarized in panel (A). TME, tumour microenvironment; ST, spatial transcriptomics; ESTIMATE, Estimation of STromal and Immune cells in MAlignant Tumours using Expression data; TNBC, triple-negative breast cancer; HER2+, human epidermal growth factor receptor 2 positive; SCEVAN, Single CEll Variational Aneuploidy aNalysis; CNA, copy-number alteration; DCIS, ductal carcinoma in situ .

Article Snippet: HER2+ Visium ST data (738811QB, 1168993F and V19L29) were downloaded from the 10x Genomics Datasets portal [ https://www.10xgenomics.com/resources/datasets (last accessed on 16 Jun 2023)].

Techniques: Dissection, Expressing, In Situ

Journal: NAR Cancer

Article Title: Spatial transcriptomics in breast cancer reveals tumour microenvironment-driven drug responses and clonal therapeutic heterogeneity

doi: 10.1093/narcan/zcae046

Figure Lengend Snippet: Spatial compartmentalization of drug response in the tumour ecosystem. ( A ) UMAP projection of spots from nine breast cancer patients clustered by their predicted sensitivity to the SSc breast. The same projection is coloured by TC (left), tumour or TME labels (centre) and major TC (right). ( B) Proportion of tumour and TME spots in each TC, coloured by major TC. The proportion of tumour spots was at least 95% in tumour-rich TCs, greater than 65%, less than 80% in mixed TCs, and 15% or less in TME-rich TCs. ( C ) Proportion of the three major TCs across all patients. ( D ) Neighbourhood enrichment between the spots defining each major TC’s edge. A z-score > 0 indicates that the spots of two categories co-localize more than expected by chance, whereas a z-score < 0 indicates a repellant effect between categories. Mean z-scores are computed to aggregate the information coming from all patients. ( E ) Spatial projection of the three major TCs on top of tissue slides from different breast cancer subtypes. ( F ) Correlation heatmap between the mean BCS of tumour and TME-rich compartments across all patients. Pearson correlation coefficients are clustered using Ward’s method. UMAP, Uniform Manifold Approximation and Projection; SSc breast, breast sensitivity signature collection; TC, therapeutic cluster; TME, tumour microenvironment; BCS, Beyondcell Score; TNBC, triple-negative breast cancer; HER2+, human epidermal growth factor receptor 2 positive.

Article Snippet: HER2+ Visium ST data (738811QB, 1168993F and V19L29) were downloaded from the 10x Genomics Datasets portal [ https://www.10xgenomics.com/resources/datasets (last accessed on 16 Jun 2023)].

Techniques:

Journal: NAR Cancer

Article Title: Spatial transcriptomics in breast cancer reveals tumour microenvironment-driven drug responses and clonal therapeutic heterogeneity

doi: 10.1093/narcan/zcae046

Figure Lengend Snippet: A functional and drug sensitivity gradient exists between the tumour and TME compartments. ( A ) Proportion of cell types in each major TC across all patients. We labelled tumour spots as cancer cells and assigned TME spots to the non-malignant cell type with maximum deconvoluted proportion. Spatial projection of ( B ) breast cancer subtype biomarkers, ( C ) drug sensitivity, ( D ) functional BCS and ( E ) the radial distances to the tumour core on top of patient CID44971 (TNBC) slide. To help visualization, we plot the square root of radial distances multiplied by the original sign. ( F ) Region-wise Pearson correlation coefficients between radial distances and functional (top) or sensitivity (bottom) BCS. Anticorrelated signatures are more enriched in spots closer to the tumour core. Correlated signatures are more enriched in the regions farthest away from the centre of the tumoural mass. The P -values were adjusted using FDR correction for multiple testing (*FDR < 0.05). TME, tumour microenvironment; TC, therapeutic cluster; BCS, Beyondcell Score; TNBC, triple-negative breast cancer; FDR, false discovery rate, CAF, cancer-associated fibroblasts; PVL, perivascular-like cell; ESR1 , Oestrogen Receptor gene; PGR , Progesterone Receptor gene; ERBB2 , Erb-B2 Receptor Tyrosine Kinase 2 gene; TLS, tertiary lymphoid structure; TIS. tumour inflammation signature; ECM, extracellular matrix; EMT, epithelial-to-mesenchymal transition; HER2+, human epidermal growth factor receptor 2 positive; anti-inflamm., anti-inflammatory; Immunosupp., immunosuppressor.

Article Snippet: HER2+ Visium ST data (738811QB, 1168993F and V19L29) were downloaded from the 10x Genomics Datasets portal [ https://www.10xgenomics.com/resources/datasets (last accessed on 16 Jun 2023)].

Techniques: Functional Assay

Journal: NAR Cancer

Article Title: Spatial transcriptomics in breast cancer reveals tumour microenvironment-driven drug responses and clonal therapeutic heterogeneity

doi: 10.1093/narcan/zcae046

Figure Lengend Snippet: The interaction with the TME influences cancer drug response. ( A ) UMAP projection of tumour spots clustered by their predicted sensitivity to the SSc breast, coloured by initial TC. ( B ) Correlation heatmap between the mean BCS of tumour TCs across all patients. Pearson correlation coefficients are clustered using Ward’s method. Initial tumour TCs were refined into TC1.1, TC1.2, TC2 and TC3. ( C ) The same projection as in panel (A), coloured by refined tumour TCs. ( D ) Bubble heatmap depicting significantly positively enriched pathways in each tumour TC, as identified by differential gene expression analysis and pre-ranked GSEA. Rows represent functional pathways, and columns represent the tumour TCs. The colour of the bubble is proportional to the NES magnitude and the size of the bubble to the FDR-adjusted P -value. Empty bubbles represent non-significant results (FDR ≥ 0.25). Rows are clustered according to the Euclidean distance between NES. ( E ) Heatmap of drugs that specifically target the tumour TCs. Drugs are clustered according to their BCS using Ward’s method and labelled by MoA. Spots from all samples are ordered by tumour TC, major TC, and the intrinsic subtype, which is determined using single-cell PAM50 signatures derived from Wu et al. TME, tumour microenvironment; UMAP, Uniform Manifold Approximation and Projection; SSc breast, breast sensitivity signature collection; TC, therapeutic cluster; BCS, Beyondcell Score; GSEA, gene set enrichment analysis; NES, Normalized Enrichment Score; FDR, false discovery rate; MoA, mechanism of action; TNBC, triple-negative breast cancer; HER2+, human epidermal growth factor receptor 2 positive; LumA, Luminal A; LumB, Luminal B.

Article Snippet: HER2+ Visium ST data (738811QB, 1168993F and V19L29) were downloaded from the 10x Genomics Datasets portal [ https://www.10xgenomics.com/resources/datasets (last accessed on 16 Jun 2023)].

Techniques: Gene Expression, Functional Assay, Derivative Assay

Journal: NAR Cancer

Article Title: Spatial transcriptomics in breast cancer reveals tumour microenvironment-driven drug responses and clonal therapeutic heterogeneity

doi: 10.1093/narcan/zcae046

Figure Lengend Snippet: Subclones display heterogeneous responses depending on their location in tumoural ROIs. Spatial projection of ( A ) ROIs, ( B ) tumour subclones and ( C ) tumour TCs on top of patient V19L29 (HER2+) tissue slides. ( D ) Sankey diagram outlining spots' ROI, subclone and tumour TC membership. ( E ) Barplots depicting the different TC compositions between the edge and inner regions of each subclone and ROI. Differences in proportions were tested with a Fisher’s exact test. The P -values were adjusted using FDR correction for multiple testing (ns FDR ≥ 0.05, *FDR < 0.05, **FDR < 0.01, ***FDR < 0.001, ****FDR < 0.0001). Spatial projection of ( F ) the CNAs and ( G ) the expression for ERBB2 biomarker, ( H ) the predicted sensitivity to the HER2 inhibitor varlitinib and ( I ) the predicted sensitivity to doxorubicin. This patient constitutes an example of pre-existent treatment resistance and the usefulness of dissecting ITH for therapy design. A combination of varlitinib and doxorubicin would target both HER2+ and HER2− cancer cells to eliminate the tumour and avoid relapse. ROI, region of interest; TC, therapeutic cluster; HER2+, human epidermal growth factor receptor 2 positive; FDR, false discovery rate; ns, not significant; CNA, copy-number alterations; ERBB2 , Erb-B2 Receptor Tyrosine Kinase 2 gene; ITH, intratumour heterogeneity; SC, subclone; BCS, Beyondcell Score.

Article Snippet: HER2+ Visium ST data (738811QB, 1168993F and V19L29) were downloaded from the 10x Genomics Datasets portal [ https://www.10xgenomics.com/resources/datasets (last accessed on 16 Jun 2023)].

Techniques: Expressing, Biomarker Discovery