Journal: Nature Communications

Article Title: SPACEL: deep learning-based characterization of spatial transcriptome architectures

doi: 10.1038/s41467-023-43220-3

Figure Lengend Snippet: a Spatial domains identified by Splane in slice S2 and S5 from Wu et al. dataset, slice S10 from Zhao et al. dataset, and slice S11 released by 10X Genomics. b , c Spatial distribution of chromosome 1q&8q copy number gains ( b ) and 1p copy number losses ( c ) of ST spots in slices S11, calculated by inferCNV. Dashed lines represent the tumor domain. d , e CNVs of chromosome 1q & 8q ( d ) and chromosome 1p ( e ) in each spatial domain calculated by inferCNV. CNVs, copy number variations; center line, median value; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; n = 11 slices. f From left to right: Splane predicted spatial domains in slice S5, distribution of Splane predicted immune domains D7/D8/D9, distribution of Spoint predicted immune cells, and distribution of H&E staining marked immune spots. g Percentage of H&E staining marked immune spots in each domain of slice S1, S2, S5, and S6. The four slices were H&E stained in the original study. Bar height, mean value; whiskers, mean values ± 95% confidence intervals; n = 4 slices. h From left to right: Splane predicted spatial domains in slice S10, distribution of Splane predicted immune domains D7, D8, and D9, distribution of Spoint predicted immune cells, and distribution of CD3 + immunofluorescence (IF) staining marked immune spots. i Percentage of CD3 + IF staining marked immune spots in each domain of slice S10. Source data are provided as a Source Data file.

Article Snippet: The raw data of 11 ST datasets and five paired single-cell/nucleus RNA sequence datasets are available from the following studies: (1) 12 slices of human DLPFC 10X Visium data at http://research.libd.org/spatialLIBD/ ; (2) six slices of human breast cancer 10X Visium data at 10.5281/zenodo.4739739 ; (3) four slices of human breast cancer 10X Visium data: Parent_Visium_Human_BreastCancer, V1_Breast_Cancer_Block_A_Section_1, V1_Breast_Cancer_Block_A_Section_2 and Visium_FFPE_Human_Breast_Cancer at https://support.10xgenomics.com/spatial-gene-expression/datasets ; (4) one slice of human breast cancer 10X Visium data: Invasive Ductal Carcinoma Stained With Fluorescent CD3 Antibody at https://support.10xgenomics.com/spatial-gene-expression/datasets ; (5) Mouse brain STARmap data at https://www.starmapresources.org/data ; (6) 33 slices of Mouse MOp MERFISH data at https://doi.brainimagelibrary.org/doi/10.35077/g.21 ; (7) one slice of mouse E16.5 embryo brain Stereo-seq data, one slice of mouse brain Stereo-seq data, and 13 slices of mouse E16.5 whole embryo Stereo-seq data at https://db.cngb.org/stomics/mosta/download/ ; (8) ten slice of human brain MERFISH data at https://datadryad.org/stash/dataset/doi:10.5061/dryad.x3ffbg7mw ; (9) 75 slice of mouse whole brain Spatial Transcriptomics data are available in the GEO database under accession number GSE147747 ; (10) single-nucleus transcriptomics data across multiple human cortical areas at https://portal.brain-map.org/atlases-and-data/rnaseq/human-multiple-cortical-areas-smart-seq ; (11) single-cell transcriptomics data of human breast cancer data at https://singlecell.broadinstitute.org/single_cell/study/SCP1039 ; (12) single-cell transcriptomics data of mouse embryo brain at http://mousebrain.org/development/downloads.html ; (13) single-cell transcriptomics data of mouse whole cortex and hippocampus at https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-10x ; (14) single-cell transcriptomics data of mouse whole brain at mousebrain.org/adolescent/downloads.html .

Techniques: Staining, Immunofluorescence

Journal: Nature Biotechnology

Article Title: Spatial multimodal analysis of transcriptomes and metabolomes in tissues

doi: 10.1038/s41587-023-01937-y

Figure Lengend Snippet: a , The SMA workflow and quality control design—nonembedded, snap-frozen samples are sectioned and thaw-mounted onto noncharged, barcoded Visium Gene Expression arrays. Tissue sections are then sprayed with MALDI matrices and MSI is performed. This is followed by H&E staining and imaging with bright field microscopy. Finally, sections are processed for SRT. We also designed the following three types of control samples: (1) MSI—samples processed with standard MALDI-MSI protocol on ITO conductive slides; (2) VISIUM—samples processed with standard Visium protocol on all four capture areas of a Visium Gene Expression array and (3) V-iCTRL—samples processed with Visium protocol, but MALDI-MSI was performed on other capture areas of a Visium Gene Expression array. b , Pairwise gene-to-gene and molecule-to-molecule correlations across biological replicates. Samples are named with short identifiers that reflect the technical conditions under which the sample was analyzed: MSI, stand-alone MALDI-MSI; SMA, SMA protocol; VISIUM, stand-alone Visium. Additional acronyms indicate the matrix used in the SMA protocol (FMP-10, DHB and 9-AA), the sample (m1, m3 or m4) and the serial number of the tissue section (one to nine for each section placed on either ITO or Visium slides). c , UMAP of SMA ST spots colored by sections (left), MALDI matrices (middle) and clusters (right). d , Top three marker genes with highest average log 2 fold change for each spatial cluster across biological replicates. e , Spatial plot of mouse brain tissue sections (striatal level, 0.49 mm from bregma) that illustrates clusters of transcripts for samples sprayed with three different MALDI matrices (FMP-10, 9-AA and DHB) and one sample processed with the stand-alone Visium protocol.

Article Snippet: Visium Spatial Gene Expression and Tissue Optimization slides, with the exception of the human postmortem sample, were processed according to the corresponding latest versions of the 10X Genomics protocols (Visium Spatial Gene Expression Reagent Kits—Tissue Optimization User Guide, document CG000238 Rev E, 10X Genomics, (February 2022); Visium Spatial Gene Expression Reagent Kits—User Guide, document CG000239 Rev F, 10X Genomics, (January 2022) and Methanol Fixation, H&E Staining and Imaging for Visium Spatial Protocols, document CG000160 Rev C, 10X Genomics), without any modification.

Techniques: Control, Gene Expression, Staining, Imaging, Microscopy, Marker

Journal: Nature Biotechnology

Article Title: Spatial multimodal analysis of transcriptomes and metabolomes in tissues

doi: 10.1038/s41587-023-01937-y

Figure Lengend Snippet: (a) Eight mouse brain tissue sections from the striatal level of the same animal (n = 8) were mounted onto a Visium Tissue Optimization slide and sprayed with four different MALDI matrices (DHB, norharmane (analyzed in both positive and negative mode, shown as Nor+ and Nor-), 9-AA and FMP-10). Areas delimited by red lines: regions of interest imaged with MALDI-MSI. Scalebars: 1 mm. (b) Representative MSI results from: i) m/z 426.36, C18:1 L-Carnitine (DHB); ii) m/z 857.52, PI(36:4) (Nor-); iii) m/z 788.62 PC(36:1) (Nor+); iv,v) m/z 303.24, arachidonic acid (9-AA); vi) m/z 371.17, GABA (FMP-10). Nor+ and Nor-: Norharmane analyzed in positive and negative mode, respectively. Scalebars: 1 mm, except iv and v where it is 2 mm. (c) Fluorescence microscopy images of mRNA footprint captured with polydT probes after MALDI-MSI. Colored lines (i, iv, vi, viii, x, xii) demarcate areas imaged with MALDI-MSI, while gray lines (ii, iii, v, vii, ix, xi, xiii, xiv) demarcate areas not imaged with MALDI-MSI and used as controls. Scalebars: 1 mm. (d) Fluorescence intensity of tissue areas imaged or not with MALDI-MSI. The upper and lower limit of the box represent the +1 and −1 standard deviation from the mean, the horizontal line inside the box represents the mean fluorescence intensity, and the upper and lower limits of the whiskers represent the maximum and minimum fluorescence intensity values. The results shown in panels (A-C) belong to eight consecutive tissue sections from n = 1 biologically independent sample examined over one independent experiment (all the sections were placed on one Visium Tissue Optimization array). The areas in square pixels over which the statistics is derived are the following: i = 768047, ii=355349, iii=843707, iv=866085, v = 578711, vi=805789, vii=562179, viii=846042, ix=317398, x = 843416, xi=611982, xii=779667, xiii=727089, xiv=751797. (e) A mouse brain tissue sections (n = 1) from the hippocampus level was mounted onto an ITO slide and sprayed FMP-10. The area delimited by a red line demarcates the region of interest imaged with MALDI-MSI. (f) Targeted In Situ Sequencing data demonstrate similar rolling circle product (RCP) density generated from MALDI-MSI processed region (upper right panel) and non-processed region (lower right panel) for demarcated regions of interest in the mouse coronal section (n = 1). Targeted ISS simultaneously probed for housekeeping gene, Gapdh labeled in Magenta (Cy5), and a panel of five control genes - Foxj1, Plp1, Lamp5, Rorb and Kcnip2 that are labeled in Cyan (AF750). (g) Mean Cy5 and AF750 fluorescence intensity of rolling circle products in tissue areas imaged or not with MALDI-MSI.The results shown in panels (E-G) belong to one tissue section from n = 1 biologically independent sample examined over one independent experiment. The number of RCPs detected in the MALDI-MSI processed region in AF750 and Cy5 and the number of RCPs detected in the non-processed region in AF750 and Cy5 respectively, which the statistics is derived from, are the following: n = 3830,n = 18231, n = 3051,n = 18193. The lower and upper hinges of the boxplot correspond to the first and third quartiles (the 25th and 75th percentiles), the central white dot corresponds to the median, the upper and lower whiskers extend from the hinge to the maximum or minimum respectively.

Article Snippet: Visium Spatial Gene Expression and Tissue Optimization slides, with the exception of the human postmortem sample, were processed according to the corresponding latest versions of the 10X Genomics protocols (Visium Spatial Gene Expression Reagent Kits—Tissue Optimization User Guide, document CG000238 Rev E, 10X Genomics, (February 2022); Visium Spatial Gene Expression Reagent Kits—User Guide, document CG000239 Rev F, 10X Genomics, (January 2022) and Methanol Fixation, H&E Staining and Imaging for Visium Spatial Protocols, document CG000160 Rev C, 10X Genomics), without any modification.

Techniques: Fluorescence, Microscopy, Standard Deviation, Derivative Assay, In Situ, Sequencing, Generated, Labeling, Control

Journal: Nature Biotechnology

Article Title: Spatial multimodal analysis of transcriptomes and metabolomes in tissues

doi: 10.1038/s41587-023-01937-y

Figure Lengend Snippet: Violin plots and box plots illustrating the number of unique genes per spot (a) and the number of unique molecular identifiers (UMIs) per spot (b) across biological conditions of the mouse striatum data (n = 9). The numbers of spots per section from which the statistics is derived are the same for the corresponding sections in panels A and B, and are the following: V-iCTRL.FMP10.mPD3.8 = 3017, V-iCTRL.nM.mPD3.3 = 3163, SMA.9AA.mPD3.4 = 2913, SMA.DHB.mPD3.1 = 2856, SMA.DHB.mPD3.2 = 3002, SMA.FMP10.mPD1.5 = 2675, SMA.FMP10.mPD3.6 = 3120, SMA.FMP10.mPD4.7 = 2918, VISIUM.mPD3.9 = 3116. n = 9 sections examined over 3 biologically independent samples. Violin plots and box plots illustrating the number of unique genes per spot (c) and the number of unique molecular identifiers (UMIs) per spot (d) of the human striatum data (n = 1). The human sample H&E was used as a legend to indicate the four capture areas A-D. The numbers of spots per capture area from which the statistics is derived are the same for corresponding sections in panels C and D and are the following: A = 4770, B = 4875, C = 4740, D = 4387. n = 4 capture areas examined over 1 biologically independent sample. For all boxplots presented in (A-D) the lower and upper hinges of the boxplot correspond to the first and third quartiles (the 25th and 75th percentiles), the central line corresponds to the median, the upper and lower whiskers extend from the hinge to the largest or smallest value respectively no further than 1.5 times the inter-quartile range, data beyond the end of the whiskers are plotted individually as black dots. On the right, spatial featureplot representing the number of genes per spot and the number of UMIs per spot of a representative capture area (that is, capture area A). (e) Sequencing metrics: i) Gene body coverage plot illustrating the sequencing coverage at different percentiles of gene body for all the genes in the quality control dataset; ii) sequencing saturation as a function of mean reads per spot; iii) median genes per spot as a function of mean reads per spot. (f) RNA integrity plots of mouse and human post-mortem samples.

Article Snippet: Visium Spatial Gene Expression and Tissue Optimization slides, with the exception of the human postmortem sample, were processed according to the corresponding latest versions of the 10X Genomics protocols (Visium Spatial Gene Expression Reagent Kits—Tissue Optimization User Guide, document CG000238 Rev E, 10X Genomics, (February 2022); Visium Spatial Gene Expression Reagent Kits—User Guide, document CG000239 Rev F, 10X Genomics, (January 2022) and Methanol Fixation, H&E Staining and Imaging for Visium Spatial Protocols, document CG000160 Rev C, 10X Genomics), without any modification.

Techniques: Derivative Assay, Sequencing, Control

Journal: Nature Biotechnology

Article Title: Spatial multimodal analysis of transcriptomes and metabolomes in tissues

doi: 10.1038/s41587-023-01937-y

Figure Lengend Snippet: (a) Scatterplots of log 10 gene counts of SMA-SRT data vs. stand-alone Visium data. The red line highlights a 1-to-1 relationship, whereas the dashed green and blue lines highlight a log 10 0.5 or −0.5 relationship. (b) Stacked barplot illustrating the percentage of genes with log 10 higher, lower or within the log 10 fold change range −0.5-0.5. The percentages inside the gray bars illustrate the percentages of peaks with absolute log 10 below 0.5.

Article Snippet: Visium Spatial Gene Expression and Tissue Optimization slides, with the exception of the human postmortem sample, were processed according to the corresponding latest versions of the 10X Genomics protocols (Visium Spatial Gene Expression Reagent Kits—Tissue Optimization User Guide, document CG000238 Rev E, 10X Genomics, (February 2022); Visium Spatial Gene Expression Reagent Kits—User Guide, document CG000239 Rev F, 10X Genomics, (January 2022) and Methanol Fixation, H&E Staining and Imaging for Visium Spatial Protocols, document CG000160 Rev C, 10X Genomics), without any modification.

Techniques:

Journal: bioRxiv

Article Title: A Pan-Cancer Single-Cell Compendium of Intratumoural Heterogeneity

doi: 10.64898/2026.01.06.693992

Figure Lengend Snippet: a, Hematoxylin and eosin (H&E) staining of breast cancer Patient 2. b-d, spatial maps of Visium HD cells coloured by cell type ( b ), mapped cancer cell state ( c ), and CNV cluster ( d ). e, Heatmap of CNV scores in malignant cells with red and blue indicating gains and losses respectively. Leiden clusters of CNV profiles are shown on the y-axis. f-g, Mean per-gene CNV values along chromosome 11 ( f ) and chromosome 8 ( g ) for each CNV cluster (0 or 1).

Article Snippet: Visium HD data and images were directly downloaded from 10X Genomics’ datasets pages – : https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-lung-cancer-post-xenium-expt , https://www.10xgenomics.com/products/visium-hd-spatial-gene-expression/dataset-human-crc , https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-breast-cancer-fixed-frozen , https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-human-breast-cancer-ff-ultima .

Techniques: Staining

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: ( A ) Spots within manually annotated LC regions containing norepinephrine (NE) neurons (red) and non-LC regions (gray), which were identified based on pigmentation, cell size, and morphology from the H&E stained histology images, from donors Br2701 (top row) and Br8079 (bottom row). ( B ) Expression of two NE neuron-specific marker genes ( TH and SLC6A2 ). Color scale indicates unique molecular identifier (UMI) counts per spot. Additional samples corresponding to A and B are shown in and . ( C ) Boxplots illustrating the enrichment in expression of two NE neuron-specific marker genes ( TH and SLC6A2 ) in manually annotated LC regions compared to non-LC regions in the n=8 Visium samples. Values show mean log-transformed normalized counts (logcounts) per spot within the regions per sample. Additional details are shown in . ( D ) Volcano plot resulting from differential expression (DE) testing between the pseudobulked manually annotated LC and non-LC regions, which identified 32 highly significant genes (red) at a false discovery rate (FDR) significance threshold of 10 –3 and expression fold-change (FC) threshold of 3 (dashed blue lines). Horizontal axis is shown on log 2 scale and vertical axis on log 10 scale. Additional details and results for 437 statistically significant genes identified at an FDR threshold of 0.05 and an FC threshold of 2 are shown in and . ( E ) Average expression in manually annotated LC and non-LC regions for the 32 genes from D . Color scale shows logcounts in the pseudobulked LC and non-LC regions averaged across n=8 Visium samples. Genes are ordered in descending order by FDR . ( F–G ) Cross-species comparison showing expression of human ortholog genes for LC-associated genes identified in the rodent LC ( ; ) using alternative experimental technologies. Boxplots show mean logcounts per spot in the manually annotated LC and non-LC regions per sample in the human data.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Staining, Expressing, Marker, Transformation Assay, Quantitative Proteomics, Comparison

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: For each of the n=9 Visium capture areas (hereafter referred to as samples), the spots were manually annotated as being within the LC regions (red) or within the non-LC regions (gray) based on spots containing NE neurons, which were identified by pigmentation, cell size, and morphology on the H&E stained histology images.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Staining

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: ( A ) Hematoxylin and eosin (H&E) stained histology images for the n=9 Visium samples. Higher-resolution images can also be viewed through the Shiny web app ( https://libd.shinyapps.io/locus-c_Visium/ ). ( B ) Estimated number of cells per spot within annotated LC regions in 6 Visium samples, based on application of cell segmentation software (VistoSeg, ). Boxplots show medians, first and third quartiles, whiskers extending to the furthest values no more than 1.5 times the interquartile range from each quartile, and outliers.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Staining, Software

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: ( A-B ) Spot-plot visualizations of NE neuron marker gene expression ( TH and SLC6A2 , A and B, respectively) in the n=9 Visium samples. Color scale shows UMI counts per spot. One sample (Br5459_LC_round2) did not show clear expression of the NE neuron marker genes. This sample was excluded from subsequent analyses, leaving n=8 Visium capture areas (samples) from 4 out of the 5 donors. The annotated LC regions are shown in . ( C ) Enrichment of NE neuron marker gene expression ( TH and SLC6A2 ) within manually annotated LC regions compared to non-LC regions in the n=8 Visium samples that passed QC. Boxplots show values as mean log-transformed normalized counts (logcounts) per spot within each region per sample, with samples represented by shapes.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Marker, Gene Expression, Expressing, Transformation Assay

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: We applied a batch integration tool (Harmony, ) to remove technical variation in the molecular measurements between the n=8 Visium samples from 4 donors. The integrated measurements were subsequently used as the input for spatially-aware clustering using BayesSpace . ( A ) Principal component analysis (PCA) (top 2 PCs) calculated on molecular expression measurements, with spots labeled (left to right) by donor ID, round ID, and sample ID, without applying any batch integration. ( B ) Harmony embeddings (top 2 Harmony embedding dimensions) after applying Harmony batch integration on sample IDs, with spots labeled (left to right) by donor ID, round ID, and sample ID, demonstrating that the technical variation has been reduced.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Expressing, Labeling

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: We applied a spatially-aware unsupervised clustering algorithm (BayesSpace, ) to investigate whether the LC and non-LC regions in each Visium sample could be annotated in a data-driven manner. ( A ) Using BayesSpace with k =5 clusters, we clustered spots from the n=8 Visium samples using the Harmony batch-integrated molecular measurements (clustering performed across samples). Cluster 4 (red) corresponds most closely to the manually annotated LC regions. The annotated LC regions are shown in . ( B ) BayesSpace clustering performance evaluated in terms of concordance between cluster 4 (red) and the manually annotated LC region in each sample. Clustering performance was evaluated in terms of precision, recall, F1 score, and adjusted Rand index (ARI) (see Methods for definitions).

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques:

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: ( A ) We manually annotated individual Visium spots (black) overlapping with NE neuron cell bodies within the previously manually annotated LC regions (red), based on pigmentation, cell size, and morphology from the H&E stained histology images, in the n=8 Visium samples. ( B ) We observed relatively low overlap between spots with expression of the NE neuron marker gene TH (≥2 observed UMI counts per spot) and the set of annotated individual spots. The differences included both false positives (annotated spots that were not TH +) and false negatives ( TH + spots that were not annotated). Therefore, we did not use the spot-level annotations for subsequent analyses, and instead used the LC region-level annotations for all further analyses.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Staining, Expressing, Marker

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: We applied nnSVG , a method to identify spatially variable genes (SVGs), in the Visium SRT samples. We ran nnSVG within each contiguous tissue area containing a manually annotated LC region (13 tissue areas in the n=8 Visium samples) and calculated an overall ranking of top SVGs by averaging the ranks per gene from each tissue area. ( A ) The top 50 ranked SVGs from this analysis included a subset (11 out of 50) of genes that were highly ranked in samples from only one donor (Br8079, genes highlighted in maroon). We determined that this was due to the inclusion of a section of the choroid plexus adjacent to the LC for this donor. Bars show the number of times (out of 13 tissue areas) each gene was included within the top 100 SVGs. Rows are ordered by overall average ranking in descending order. ( B ) Spatial expression of CAPS , a choroid plexus marker gene, in the n=8 Visium samples. ( C ) Histology image showing the two tissue areas for sample Br8079_LC_round3. ( D ) In order to focus on LC-associated SVGs, we calculated an overall average ranking of SVGs that were each included within the top 100 SVGs in at least 10 out of the 13 tissue areas, which identified 32 highly-ranked, replicated LC-associated SVGs. Boxplots show the ranks in each tissue area. Rows are ordered by the overall average ranking in descending order.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Expressing, Marker

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: ( A-B ) We visualized the spatial expression of 5-HT (5-hydroxytryptamine or serotonin) neuron marker genes ( TPH2 and SLC6A4 ) in the n=9 initial Visium SRT samples within the Visium SRT samples, which showed that the population of 5-HT neurons was distributed across both the LC and non-LC regions. The annotated LC regions are shown in . ( C ) Enrichment of 5-HT neuron marker gene expression ( TPH2 and SLC6A4 ) within manually annotated LC regions compared to non-LC regions in the n=8 Visium SRT samples that passed QC (see ). Boxplots show values as mean log-transformed normalized counts (logcounts) per spot within each region per sample, with samples represented by shapes.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Expressing, Marker, Gene Expression, Transformation Assay

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: We visualized the spatial expression of ( A-B ) additional marker genes for 5-HT neurons ( SLC18A2 , FEV ) and ( C-D ) 5-HT autoreceptor genes ( HTR1A , HTR1B ) in the n=9 initial Visium samples. SLC6A18 is not shown since we observed zero expression of this gene in the Visium samples. Color scale shows UMI counts per spot.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Expressing, Marker

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: We applied a spot-level deconvolution algorithm (cell2location, ) to integrate the snRNA-seq and SRT data by estimating the cell abundance of the snRNA-seq populations, which are used as reference populations, at each spatial location (spot) in the Visium SRT samples. While this approach mapped ( A ) NE neurons (cluster 6) and ( B ) 5-HT neurons (cluster 21) to the spatial regions where these populations were previously identified based on expression of marker genes ( and ), the overall mapping performance was relatively poor. We note that these are relatively rare populations, with relatively subtle expression differences compared to other neuronal populations, and NE neurons are characterized by large size and high transcriptional activity, which may have affected performance of the algorithm. The annotated LC regions are shown in .

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Expressing, Marker, Activity Assay

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: We visualized the spatial expression of DA neuron marker genes ( A ) SLC6A3 (encoding the dopamine transporter), ( B ) ALDH1A1 , and ( C ) SLC26A7 in the n=9 initial Visium SRT samples, which showed that these genes were not strongly expressed within the annotated LC regions. Color scale shows UMI counts per spot. The annotated LC regions are shown in .

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Expressing, Marker

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: We visualized the spatial expression of cholinergic marker genes ( A ) SLC5A7 and ( B ) ACHE in the n=9 initial Visium SRT samples, which showed that these genes were expressed both within and outside the annotated LC regions. Color scale shows UMI counts per spot. The annotated LC regions are shown in .

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques: Expressing, Marker

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: Summary of data resources providing access to datasets described in this manuscript. All datasets described in this manuscript are freely accessible in the form of interactive web apps and downloadable R/Bioconductor objects.

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

Techniques:

Journal: eLife

Article Title: The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

doi: 10.7554/eLife.84628

Figure Lengend Snippet: All datasets described in this manuscript are freely accessible via interactive web apps and downloadable R/Bioconductor objects (see for details). ( A ) Screenshot of Shiny web app providing interactive access to Visium SRT data. ( B ) Screenshot of iSEE web app providing interactive access to snRNA-seq data. For instructions on how to use the web apps to search for and display individual genes, see and .

Article Snippet: For tissue blocks included in the study, we cut additional 10 μm tissue sections, which were used for gene expression profiling at spatial resolution using the 10x Genomics Visium SRT platform ( ; ).

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 , 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: Science Advances

Article Title: A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types

doi: 10.1126/sciadv.adh7693

Figure Lengend Snippet: ( A ) Molecular cell type–based neuroanatomy of the goldfish telencephalon. Top: Color scheme shown above for GABAergic and glutamatergic cell types (dendrogram). Center: Eight coronal goldfish sections (goldfish 1), sampled for Visium ST, overlaid with a weighted color map that integrates all goldfish telencephalon cell types. Bottom: Regional parcellation based on color map differences above, each color indicates a different region, and suggested nomenclature annotated by similarity region names according to Northcutt . D , area dorsalis; V , area ventralis; Dc , large-celled subdivision of Dm ; Vsst , ventral Sst; Ppa , nucleus preopticus parvocellularis anterioris; a, anterior; p, posterior; d, dorsal; v, ventral; m, medial; l, lateral. ( B ) Heatmaps of SD (normalized per row) along lateral-medial (left) and dorsal-ventral (right) axes for top axial pattern genes, for goldfish 1 and 2; dots indicate spatial enrichment according to axial color scheme shown above; gray without dot, no enrichment. Right: Summary of axial score per gene (mean of enriched sections). ( C ) Expression of 14 axial-patterned genes across the goldfish telencephalon. Gray, low; red, high.

Article Snippet: ST (Visium, 10x Genomics) is a sequencing-based, transcriptome-wide in situ mRNA detection method; at the loss of cellular resolution, the method retained positional information of mRNA molecules, per 55-μm-diameter capture spots, that are arranged in an array with centers ( X - Y ) 100 μm apart.

Techniques: Expressing

Journal: Science Advances

Article Title: A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types

doi: 10.1126/sciadv.adh7693

Figure Lengend Snippet: ( A ) t -SNE visualization of GABAergic neurons in the goldfish forebrain. Each dot represents a cell, colored by cell type assignment. Right: Expression of three branch-organizing genes. ( B ) All GABA types arranged in dendrogram order (GABA1 to GABA40), with top marker gene expression visualized as heatmap (white, high; black, low). Middle: Violin plots, where each dot represents a single cell; maximum expression (UMI) indicated on the right. Bottom: Contribution of four microdissections to each cell type, visualized as pie charts. ( C ) Expression of three branch-organizing genes [as (A)] and, in ST, eight anterior-posterior telencephalon coronal hemisphere sections. ( D ) Examples across the GABAergic dendrogram for spatial correlation of Visium spots: five scRNA-seq cell types (columns) across eight a.-p. coronal sections (rows).

Article Snippet: ST (Visium, 10x Genomics) is a sequencing-based, transcriptome-wide in situ mRNA detection method; at the loss of cellular resolution, the method retained positional information of mRNA molecules, per 55-μm-diameter capture spots, that are arranged in an array with centers ( X - Y ) 100 μm apart.

Techniques: Expressing, Marker, Gene Expression

Journal: Science Advances

Article Title: A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types

doi: 10.1126/sciadv.adh7693

Figure Lengend Snippet: ( A ) t -SNE visualization of glutamatergic neurons in the goldfish forebrain. Each dot represents a cell, colored by cell type. ( B ) All glutamatergic types, in dendrogram order (GLUT1 to GLUT48), with top marker gene expression visualized as heatmap (white, high; black, low). Middle: Violin plots, where each dot represents a single cell; maximum expression (UMI) indicated on the right. Bottom: Contribution of four microdissections to each cluster, visualized as pie charts. ( C ) Expression of two branch-organizing genes, NR2F2 and CNR1, visualized on t -SNE [as (A)] and, in ST, eight anterior-posterior telencephalon coronal hemisphere sections. ( D ) Examples across the glutamatergic dendrogram for spatial correlation of Visium spots: four scRNA-seq cell types (columns) across eight anterior-posterior coronal sections (rows).

Article Snippet: ST (Visium, 10x Genomics) is a sequencing-based, transcriptome-wide in situ mRNA detection method; at the loss of cellular resolution, the method retained positional information of mRNA molecules, per 55-μm-diameter capture spots, that are arranged in an array with centers ( X - Y ) 100 μm apart.

Techniques: Marker, Gene Expression, Expressing

Journal: Science Advances

Article Title: A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types

doi: 10.1126/sciadv.adh7693

Figure Lengend Snippet: ( A and B ) t -SNE visualizing comparative species analysis between goldfish and zebrafish telencephalon, with cell types highlighted per species; (A) goldfish and (B) zebrafish. Per cell class, both species’ datasets are integrated (Harmony), followed by DBSCAN clustering. ( C ) Per cell type comparison, scored using KNN classifier. ( D ) Expression of CBLN1, PENK , and SST in the integrated teleostean dataset; dots (cells) colored by species origin. ( E ) Validation of gene expression detected in ST using HCR-FISH. Top row: Corresponding section overviews of genes detected in Visium (left) and HCR (right), where each spot represents a segmented fluorescent cell, colored by normalized expression. Bottom row: Raw fluorescent signal in zoom-ins, as indicated in overview sections.

Article Snippet: ST (Visium, 10x Genomics) is a sequencing-based, transcriptome-wide in situ mRNA detection method; at the loss of cellular resolution, the method retained positional information of mRNA molecules, per 55-μm-diameter capture spots, that are arranged in an array with centers ( X - Y ) 100 μm apart.

Techniques: Comparison, Expressing, Biomarker Discovery, Gene Expression

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: a Computational resolution enhancement cannot achieve single-cell level. Illustration of the spot layout on the mouse brain hippocampus 10X Visium FFPE spatial transcriptome (left), the original gene expression summary at the spot level (middle), the BayesSpace-imputed gene expression summary at the subspot level along with the enlarged subspots on the H&E image (right). In the enlarged area, the white circle represents the original spot, and the red circle represents the enhanced subspot. b–e Systematic evaluation of spatial relationship between single cells and spots via simulation of high-resolution spots from real 10X Visium spatial transcriptomics FFPE data. b Nuclear morphological feature distribution of mouse kidney, mouse brain and human breast cancer. c Examples of simulated high-resolution spots on the real nuclear segmentation. The brown circle represents the high-resolution spot with 5 μm in diameter, and the black circle represents the nucleus in the real tissue. d The frequency of spots covering different numbers of cells, where the x-axis is the cell count covered by one single spot, and the y-axis represents the spot frequency. Only the spot that covers cells was considered. e The distribution of the cell area fraction covered by the spots in different diameters. In the box plots ( b , e ), center line represents median, lower and upper hinges represent first and third quartiles, whiskers extend from hinge to ±1.5 × IQR. The above distributions are drawn from 172,835 nuclei in mouse kidney, 31,546 nuclei in mouse brain, and 44,218 nuclei in human breast cancer, respectively. Source data are provided as a Source Data file.

Article Snippet: The raw reads and images of 10X ST Visium FFPE for mouse brain, mouse kidney and human breast cancer and the processed gene-level read count matrix and image of 10X CytAssist Spatial Gene Expression (V2 Chemistry) for mouse brain section 1&2 were downloaded from www.10xgenomics.com/resources .

Techniques: Gene Expression, Cell Counting

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: a–j Mouse brain hippocampus 10X Visium FFPE spatial transcriptome. a Spot-level cell-type deconvolution using SPOTlight. In the enlarged area, each pie chart represents the proportion of cell types for the corresponding spot. b Subspot-level cell type deconvolution using BayesSpace followed by SPOTlight. In the enlarged area, each pie chart represents the proportion of cell types for the corresponding subspot. c Single-cell level deconvolution by STIE (left panel), which is the aggregation of cells captured by spots (middle panel) and cells missed by spots but recovered by STIE (right panel). In the enlarged area, the circle is the cell contour, with the color representing its cell type. Spot-level cell-type deconvolution using DWLS ( d ), Stereoscope ( e ), RCTD ( f ), Tangram ( g ), and BayesPrism ( h ). i Ground truth of mouse brain hippocampus cell types by In Situ Hybridization (ISH): CA1 ( Mpped1 ), CA2 ( Map3k15 ), CA3 ( Cdh24 ) and DG ( Prox1 ). The arrowhead indicates high expression. The figure is reproduced from Fig. in ref. . j Nuclear morphological feature distributions of cell types learned by STIE (422 CA1, 39 CA2, 115 CA3, 818 DG, and 872 Glia). k Single-cell level deconvolution by STIE on 10X Visium V2 Chemistry CytAssist FFPE spatial transcriptomics of two consecutive mouse brain hippocampus sections: section 1 (up panel) and section 2 (bottom panel). l–n Human breast cancer 10X Visium FFPE spatial transcriptome. l Single-cell level deconvolution by STIE. m Nuclear morphological feature distributions for cell types (2910 Bcells, 12,269 CAFs, 11,957 CancerEpithelial, 3120 Myeloid, 6997 Plasmablasts, 1920 PVL, and 4584 Tcells). Center line represents median, lower and upper hinges represent first and third quartiles, whiskers extend from hinge to ±1.5× IQR. n Manually annotated human breast cancer pathological regions. Single-cell convolution for the simulated high-resolution spatial transcriptomics data of the mouse brain hippocampus ( o ) and human breast cancer ( p ) using spots with 5 μm in diameter. Source data are provided as a Source Data file.

Article Snippet: The raw reads and images of 10X ST Visium FFPE for mouse brain, mouse kidney and human breast cancer and the processed gene-level read count matrix and image of 10X CytAssist Spatial Gene Expression (V2 Chemistry) for mouse brain section 1&2 were downloaded from www.10xgenomics.com/resources .

Techniques: In Situ Hybridization, Expressing

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: Cell type specific transcriptomic signature learning from 10X Visium mouse brain hippocampus FFPE ( a ) and 10X Visium human breast cancer FFPE ( b ). Spot-level clustering by K-means, SpaGCN, MUSE, subspot-level clustering by BayesSpace and single-cell-level clustering by STIE on 10X Visium FFPE mouse brain hippocampus ( c ), mouse brain cortex ( e ) and human breast cancer ( g ). Cell type deconvolution of spot-, subspot-, and single-cell-level clustering-derived CAGE in the mouse brain hippocampus ( d ), mouse brain cortex ( f ), and human breast cancer ( h ). For the mouse brain cortex, the cell types in the transcriptomic signature, which are not cortex layers and have small proportions, are not shown in the barplot. The box plot ( h ) represents the deconvoluted proportion of 9 cell types, where center line represents median, lower and upper hinges represent first and third quartiles, and whiskers extend from hinge to ±1.5 × IQR. The p-value was calculated based on one-sided Wilcoxon signed-rank test without adjustment for multiple comparisons. i The UMAP plot of human breast cancer scRNA-seq data from 26 primary tumors . The top panel is the original cell typing of 10,060 single cells, and the bottom panel is the subset of cells that are mapped to the six STIE clusters. Spot-level clustering by K-means (left), SpaGCN (middle), and single-cell-level clustering by STIE (right) on the simulated high-resolution spot spatial transcriptome data of the mouse brain hippocampus ( j ) and human breast cancer ( m ). Cell type deconvolution of spot- and single-cell-level clustering-derived CAGE in the mouse brain hippocampus ( k ) and human breast cancer ( n ). l , o The consistency table of single-cell clusters between the simulated high-resolution spot-based STIE clustering and the original low-resolution spot-based STIE clustering as ground truth of the mouse brain hippocampus ( c ) and human breast cancer ( g ). Source data are provided as a Source Data file.

Article Snippet: The raw reads and images of 10X ST Visium FFPE for mouse brain, mouse kidney and human breast cancer and the processed gene-level read count matrix and image of 10X CytAssist Spatial Gene Expression (V2 Chemistry) for mouse brain section 1&2 were downloaded from www.10xgenomics.com/resources .

Techniques: Derivative Assay

Journal: Nature Communications

Article Title: STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics

doi: 10.1038/s41467-024-51728-5

Figure Lengend Snippet: Identification of the bona fide area captured by spots in the mouse brain hippocampus ( a ) and human breast cancer ( b ). The x-axis represents the putative size of the bona fide area measured by the spot in the unit of a regular 10X Visium spot size (55 μm). The top panel represents the chart of bona fide spot size in the real tissue. The middle panel represents the cell count (y-axis) in the spot area (x-axis); the bottom panel represents the RMSE by fitting the STIE model using the cells within the corresponding area indicated by the x-axis. The distributions are drawn from 80 to 151 spots in mouse brain hippocampus and 1568–2508 spots in human breast cancer, respectively. c Identification of the bona fide area captured by spots in the 10X Visium V2 Chemistry CytAssist mouse brain hippocampus. The distributions are drawn from 120 to 230 spots in section 1 and 121–245 spots in section 2, respectively. The evaluation of image contribution to the cell type deconvolution in human breast cancer ( d ) and mouse brain hippocampus ( e ). The top panel represents the difference between cell-type proportions estimated from the spot gene expression and the nuclear morphological features; the bottom panel represents the RMSE of gene expression fitting. The x-axis represents the value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document} λ in Formula (7). The distributions are drawn from 151 spots in mouse brain hippocampus and 2,451 spots in human breast cancer, respectively, which are presented as mean values ±SEM. f Heatmap of the correlation between the cell type proportion within spots by SPOTlight, DWLS, Stereoscope, RCTD, Tangram, BayesPrism, and STIE. g The association between transcriptomic signature similarity and cell-type colocalization by SPOTlight, DWLS, Stereoscope, RCTD, Tangram, BayesPrism, and STIE. The two-sided p values are calculated for the Pearson’s correlation coefficients ( n = 36) without adjustment for multiple comparisons. h–i High-resolution spots along with STIE holds the premise to distinguish nuanced cell types. h Random assignments of Memory Bcell or Naïve Bcell to the Bcell; CD8+ Tcell, CD4+ Tcell, NK cells, Cycling Tcell, or NKT cell, to the Tcell; and Macrophage, Monocyte, Cycling Myeloid, or DCs to the Myeloid. i The barplot represents the concordance of STIE deconvoluted/convoluted single cells with the simulation ground truth ( h ). The x-axis represents the simulated spot diameter, and the y-axis represents the concordance. The color refers to the cell type in the legend. Source data are provided as a Source Data file.

Article Snippet: The raw reads and images of 10X ST Visium FFPE for mouse brain, mouse kidney and human breast cancer and the processed gene-level read count matrix and image of 10X CytAssist Spatial Gene Expression (V2 Chemistry) for mouse brain section 1&2 were downloaded from www.10xgenomics.com/resources .

Techniques: Cell Counting, Gene Expression