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10X Genomics 760 spatial transcriptomic profiling
760 Spatial Transcriptomic Profiling, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/transcriptomic+profiles/data+spatial+transcriptomic/pm42167485-330-12-20
Average 86 stars, based on 1 article reviews
760 spatial transcriptomic profiling - by Bioz Stars, 2026-09
86/100 stars

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Related Articles

Spatial Transcriptomics:

Article Title: Single-cell multiomics gene regulatory landscape reveals impaired spermatogonial stem cells and macrophage-driven inflammaging during testicular aging.
Article Snippet: 29 Testicular aging is a key driver of declining male reproductive health, but a comprehensive 30 understanding of its underlying epigenetic drivers is lacking.. To address this, we construct a 31 multiomics aging atlas by integrating single-cell RNA sequencing, single-cell assay for 32 transposase-accessible chromatin sequencing (scATAC-seq), and spatial transcriptomics of 33 young and aged mouse testes.. Our analysis reveals that altered chromatin accessibility 34 accompanies transcriptional dysregulation and identifies spermatogonial stem cells (SSCs) as 35 the most epigenetically vulnerable population.

Sequencing:

Article Title: Single-cell multiomics gene regulatory landscape reveals impaired spermatogonial stem cells and macrophage-driven inflammaging during testicular aging.
Article Snippet: 29 Testicular aging is a key driver of declining male reproductive health, but a comprehensive 30 understanding of its underlying epigenetic drivers is lacking.. To address this, we construct a 31 multiomics aging atlas by integrating single-cell RNA sequencing, single-cell assay for 32 transposase-accessible chromatin sequencing (scATAC-seq), and spatial transcriptomics of 33 young and aged mouse testes.. Our analysis reveals that altered chromatin accessibility 34 accompanies transcriptional dysregulation and identifies spermatogonial stem cells (SSCs) as 35 the most epigenetically vulnerable population.

Article Title: Spatial single-cell landscape of tumor-associated macrophages and their crosstalk with the tumor microenvironment.
Article Snippet: .. To minimize batch effects caused by differences in sequencing platforms and methodologies, all single-cell and spatial transcriptomic data were obtained exclusively from the 10x Genomics and 10x Visium platforms. ..

Gene Expression:

Article Title: Single-cell multiomics gene regulatory landscape reveals impaired spermatogonial stem cells and macrophage-driven inflammaging during testicular aging.
Article Snippet: 29 Testicular aging is a key driver of declining male reproductive health, but a comprehensive 30 understanding of its underlying epigenetic drivers is lacking.. To address this, we construct a 31 multiomics aging atlas by integrating single-cell RNA sequencing, single-cell assay for 32 transposase-accessible chromatin sequencing (scATAC-seq), and spatial transcriptomics of 33 young and aged mouse testes.. Our analysis reveals that altered chromatin accessibility 34 accompanies transcriptional dysregulation and identifies spermatogonial stem cells (SSCs) as 35 the most epigenetically vulnerable population.

Single Cell:

Article Title: Spatial single-cell landscape of tumor-associated macrophages and their crosstalk with the tumor microenvironment.
Article Snippet: .. To minimize batch effects caused by differences in sequencing platforms and methodologies, all single-cell and spatial transcriptomic data were obtained exclusively from the 10x Genomics and 10x Visium platforms. ..

Article Title: The Role of Tumor Necrosis Factor Signaling in Atherosclerosis and Stroke
Article Snippet: .. To characterise TNF signaling within atherosclerotic plaques, we analysed two publicly available datasets: (i) an integrated single-cell RNA-sequencing (scRNA-seq) atlas of 259,116 cells from human carotid, coronary, and femoral plaques (73 donors), and (ii) Xenium (10x Genomics) spatial transcriptomic data comprising 120,164 cells from carotid endarterectomy specimens with pathologist-annotated subregions (12 donors). ..

In Situ:

Article Title: SARS-CoV-2 infection and vaccination elicit distinct pharyngeal mucosal B cell responses in children.
Article Snippet: .. Spatial transcriptomic profiling with Xenium In Situ platform Slides were prepared following the manufacturer’s instructions and workflow for FFPE tissue samples (CG000578 Rev A; 10x Genomics). .. A 5-μm section from the tissue block containing the same paired tonsil and adenoid samples (one from INF donor and one from VAC donor) used for immunofluorescence were carefully attached to the sample area on a Xenium slide (Histoserv, MD).

Article Title: An antioxidant therapy elicits distinct transcriptome responses in 22q11-deleted upper layer cortical projection neurons.
Article Snippet: .. To assess L 2/3 PN transcriptional responses that underlie NAC’s therapeutic effects in vivo, we first established that spatial transcriptomic RNA quantification in situ (10X Genomics Xenium) securely identifies L 2/3 PNs and their neighbors, thus ensuring that transcriptional states can be assessed in intact cortices of early post-natal WT, LgDel, LgDel + NAC and WT + NAC L 2/3 mice. ..

Article Title: Won't you be my neighbor? Control of the immune response by stromal and immune cell microenvironments within the lymph node.
Article Snippet: Efficacious immune responses require the coordinated encounter of rare antigen-specific adaptive lymphocytes with their cognate innate antigen-presenting cells (APCs) in space and time.. This spatiotemporal problem of immunity is solved by secondary lymphoid organs, such as lymph nodes (LNs), which coordinate adaptive immune responses by recruiting APCs and lymphocytes into close juxtaposition with tissue antigens drained from the periphery.. A central tenet to the overall function of the LN is the spatial organization of leukocytes into discrete microenvironments orchestrated by the mesenchymal and endothelial cells, collectively termed LN stromal cells (LNSCs).

Formalin-fixed Paraffin-Embedded:

Article Title: SARS-CoV-2 infection and vaccination elicit distinct pharyngeal mucosal B cell responses in children.
Article Snippet: .. Spatial transcriptomic profiling with Xenium In Situ platform Slides were prepared following the manufacturer’s instructions and workflow for FFPE tissue samples (CG000578 Rev A; 10x Genomics). .. A 5-μm section from the tissue block containing the same paired tonsil and adenoid samples (one from INF donor and one from VAC donor) used for immunofluorescence were carefully attached to the sample area on a Xenium slide (Histoserv, MD).

In Vivo:

Article Title: An antioxidant therapy elicits distinct transcriptome responses in 22q11-deleted upper layer cortical projection neurons.
Article Snippet: .. To assess L 2/3 PN transcriptional responses that underlie NAC’s therapeutic effects in vivo, we first established that spatial transcriptomic RNA quantification in situ (10X Genomics Xenium) securely identifies L 2/3 PNs and their neighbors, thus ensuring that transcriptional states can be assessed in intact cortices of early post-natal WT, LgDel, LgDel + NAC and WT + NAC L 2/3 mice. ..

RNA sequencing:

Article Title: The Role of Tumor Necrosis Factor Signaling in Atherosclerosis and Stroke
Article Snippet: .. To characterise TNF signaling within atherosclerotic plaques, we analysed two publicly available datasets: (i) an integrated single-cell RNA-sequencing (scRNA-seq) atlas of 259,116 cells from human carotid, coronary, and femoral plaques (73 donors), and (ii) Xenium (10x Genomics) spatial transcriptomic data comprising 120,164 cells from carotid endarterectomy specimens with pathologist-annotated subregions (12 donors). ..



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(a-d) t-SNE performed on (a) PBMC immune frequencies, (b) PBMC gene expression, (c) immune frequencies and gene expression combined, and (d) plasma proteomics. (e) Clustering of transcriptomic data from our study and the Human Protein Atlas. (f) Distribution of Euclidean distance in transcriptomic profiles between collected samples (i) from the same participant within the first year (visit 1-4), (ii) from the same participant between year 1 and year 2 (visit 5-6), and (iii) from different participants. Wilcoxon tests were used for statistical analysis (ns, not significant; ****, P < 0.0001). (g) Euclidean distance between each transcriptomic profile from the last visit and the sample collected during the first year, divided into (i) pairs of samples from the same individual (light blue) and (ii) pairs of samples from different participants (red). (h) Distribution of intraclass correlation coefficient of immune cell profiling, transcriptomic and plasma proteomics. (i) Intraclass correlation coefficient of all 53 immune populations. (j) Intra- and inter-individual coefficients of variation of gene expression. (k) Examples of genes, immune populations and proteins showing individual expression profiles. Samples are colored according to their expression levels at visit 1 (orange: 0-25%, green: 25–50%, blue: 50–75%, purple: 75–100%). The highlighted dots indicate the median level of the corresponding group at that visit.

Journal: bioRxiv

Article Title: Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

doi: 10.64898/2026.03.21.713378

Figure Lengend Snippet: (a-d) t-SNE performed on (a) PBMC immune frequencies, (b) PBMC gene expression, (c) immune frequencies and gene expression combined, and (d) plasma proteomics. (e) Clustering of transcriptomic data from our study and the Human Protein Atlas. (f) Distribution of Euclidean distance in transcriptomic profiles between collected samples (i) from the same participant within the first year (visit 1-4), (ii) from the same participant between year 1 and year 2 (visit 5-6), and (iii) from different participants. Wilcoxon tests were used for statistical analysis (ns, not significant; ****, P < 0.0001). (g) Euclidean distance between each transcriptomic profile from the last visit and the sample collected during the first year, divided into (i) pairs of samples from the same individual (light blue) and (ii) pairs of samples from different participants (red). (h) Distribution of intraclass correlation coefficient of immune cell profiling, transcriptomic and plasma proteomics. (i) Intraclass correlation coefficient of all 53 immune populations. (j) Intra- and inter-individual coefficients of variation of gene expression. (k) Examples of genes, immune populations and proteins showing individual expression profiles. Samples are colored according to their expression levels at visit 1 (orange: 0-25%, green: 25–50%, blue: 50–75%, purple: 75–100%). The highlighted dots indicate the median level of the corresponding group at that visit.

Article Snippet: Moreover, we downloaded the transcriptomic profiles available from the Human Protein Atlas ( https://www.proteinatlas.org/humanproteome/blood ) and used them to verify the expression patterns of key genes across cell types.

Techniques: Gene Expression, Clinical Proteomics, Expressing

(a) t-SNE sample clustering based on transcriptomic profiles of genes in the PBMC association network. (b) Immune frequencies patterns across the three sample clusters. (c) Distribution of (top) innate immune cell frequencies and (bottom) CD4:CD8 ratio across the three clusters. (d) Immune frequencies of the modules in the PBMC network, divided by sample clusters. (e) Number of up- and down-regulated genes in each module (FDR<0.05) obtained from differential expression analysis between each cluster and the remaining two. (f-h) t-SNE clustering colored based on CD4:CD8+ T cells ratio, and immune frequencies of the B cells, cytotoxic and myeloid modules. (j) (Top) CRP levels of participant P3920. (Bottom) Sample clusters of participants across visits; highlighted in black are the samples from participant P3920. (k) Pattern of clinical variables across the three clusters. (l) Patterns of the 30 proteins with the most significant up-regulation in any of the three clusters. SBP, systolic blood pressure; DBP, diastolic blood pressure; TNT, troponin T; CRP, C-reactive protein; HDL, high density lipoprotein; ALAT, alanine aminotransferase; GGT, gamma-glutamyl transferase. P-values are calculated by Wilcoxon tests in c-d; ns, not significant; * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Journal: bioRxiv

Article Title: Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

doi: 10.64898/2026.03.21.713378

Figure Lengend Snippet: (a) t-SNE sample clustering based on transcriptomic profiles of genes in the PBMC association network. (b) Immune frequencies patterns across the three sample clusters. (c) Distribution of (top) innate immune cell frequencies and (bottom) CD4:CD8 ratio across the three clusters. (d) Immune frequencies of the modules in the PBMC network, divided by sample clusters. (e) Number of up- and down-regulated genes in each module (FDR<0.05) obtained from differential expression analysis between each cluster and the remaining two. (f-h) t-SNE clustering colored based on CD4:CD8+ T cells ratio, and immune frequencies of the B cells, cytotoxic and myeloid modules. (j) (Top) CRP levels of participant P3920. (Bottom) Sample clusters of participants across visits; highlighted in black are the samples from participant P3920. (k) Pattern of clinical variables across the three clusters. (l) Patterns of the 30 proteins with the most significant up-regulation in any of the three clusters. SBP, systolic blood pressure; DBP, diastolic blood pressure; TNT, troponin T; CRP, C-reactive protein; HDL, high density lipoprotein; ALAT, alanine aminotransferase; GGT, gamma-glutamyl transferase. P-values are calculated by Wilcoxon tests in c-d; ns, not significant; * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: Moreover, we downloaded the transcriptomic profiles available from the Human Protein Atlas ( https://www.proteinatlas.org/humanproteome/blood ) and used them to verify the expression patterns of key genes across cell types.

Techniques: Quantitative Proteomics