rna rescue spatial transcriptomics rrst  (Spatial Transcriptomics Inc)

Journal: Nature Communications

Article Title: Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

doi: 10.1038/s41467-023-36071-5

Figure Lengend Snippet: a H&E images of a representative tissue section from mouse brain (left) and prostate cancer (right). The entire dataset consisted of 8 consecutive mouse brain tissue sections and 4 consecutive prostate cancer tissue sections. Half of the tissue sections were processed with RRST and the remaining half with standard Visium protocol. b Spatial distribution of unique genes in two representative tissue sections for each tissue type, one processed with the RRST protocol and one processed with the standard Visium protocol. c Distributions of unique genes per spot visualized as violin/box plots colored by experimental protocol for mouse brain and prostate cancer data. Box plots are presented as median values where the lower and upper bounds are the 25th and 75th percentiles. The upper and lower limits of the boxplots are defined by the closest value no further than 1.5*IQR (inter-quartile range) from the closest bound. Values outside of the upper and lower limits are highlighted as outliers. The median number of unique genes is highlighted for each group (sample type and protocol) next to the violin plots. d gene-gene scatter plots between RRST data ( y -axis) and standard Visium data ( x -axis) of log1p-transformed UMI counts and detection rates using the data shown in ( b ). The UMI counts and detection rates were calculated across the pooled technical replicates within each experimental protocol. The red dashed line highlights a 1-to-1 relationship. For the log1p-transformed UMI counts scatter plot, only genes targeted by the probe panel were included. The detection rate for a gene is defined as the proportion of spots with detected UMI counts. The statistical test is based on the Pearson product moment correlation coefficient and p -values were estimated using a two-sided alternative hypothesis.

Article Snippet: Here, authors present a genome-wide spatial gene expression profiling method called RNA Rescue Spatial Transcriptomics (RRST), designed for the analysis of moderate to low quality fresh frozen tissue samples and demonstrate its robustness on 7 different tissue types.

Techniques: Transformation Assay

Journal: Nature Communications

Article Title: Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

doi: 10.1038/s41467-023-36071-5

Figure Lengend Snippet: Each subplot shows the RRST data on the left side and the standard Visium data on the right side. a H&E images of two representative tissue sections collected from the same tissue block. b Violin/box plots showing the distribution of unique genes and UMI counts for RRST ( n = 1) and standard Visium ( n = 2) data generated from consecutive tissue sections from the same lung tissue specimen. The y -axis is shown in log10 scale. Box plots are presented as median values where the lower and upper bounds are the 25th and 75th percentiles. The upper and lower limits of the boxplots are defined by the closest value no further than 1.5*IQR (inter-quartile range) from the closest bound. Values outside of the upper and lower limits are highlighted as outliers. c Unique genes per spot mapped on tissue coordinates. d Spatial visualization showing what spots were discarded due to low quality (less than 300 unique genes detected). e UMAP embedding of adult lung data colored by clusters detected by unsupervised graph-based clustering (louvain). f Split view of clusters (same as in e ) mapped on tissue coordinates. g Dot plots of the top marker genes for each cluster. Each cluster was annotated based on its spatial localization in the tissue and expression of canonical marker genes.

Article Snippet: Here, authors present a genome-wide spatial gene expression profiling method called RNA Rescue Spatial Transcriptomics (RRST), designed for the analysis of moderate to low quality fresh frozen tissue samples and demonstrate its robustness on 7 different tissue types.

Techniques: Blocking Assay, Generated, Marker, Expressing

Journal: Nature Communications

Article Title: Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

doi: 10.1038/s41467-023-36071-5

Figure Lengend Snippet: a Representative H&E images and annotated regions for two patient samples processed by either RRST ( n = 4) or standard Visium ( n = 2) protocol. The spots in each tissue section were labeled into three categories: mucosa, submucosa, and muscularis. b Distribution of UMI counts in the tissue sections shown in ( a ). The color scale represents log10-transformed counts. c Distribution of unique genes per spot in the three annotated regions (mucosa, submucosa, and muscularis) visualized as violin plots, for all tissue sections. The y -axis shows log10-transformed counts. d Distribution of UMI counts per spot in the three annotated regions (mucosa, submucosa, and muscularis) visualized as violin plots. The y -axis shows log10-transformed counts. e Expression of 11 epithelial markers in the mucosa for the two adult colon samples visualized as violin plots. A comparison between the two protocols is shown for each gene and the corresponding detection rate is highlighted below each violin plot. The detection rate is defined as the percentage of spots (in the mucosa) where the gene is detected.

Article Snippet: Here, authors present a genome-wide spatial gene expression profiling method called RNA Rescue Spatial Transcriptomics (RRST), designed for the analysis of moderate to low quality fresh frozen tissue samples and demonstrate its robustness on 7 different tissue types.

Techniques: Labeling, Transformation Assay, Expressing, Comparison

Journal: Nature Communications

Article Title: Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

doi: 10.1038/s41467-023-36071-5

Figure Lengend Snippet: a Representative H&E image (top) and spots colored by five major tissue regions (bottom): mucosa, TLS, submucosa, muscularis, and serosa. TLS, Tertiary Lymphoid Tissue. The full small intestine dataset consisted of 14 tissue sections collected from the same specimen at different time points. Only sections collected at the last point were processed with RRST, while the other sections were processed with standard Visium protocol. b Overview of data quality in the five annotated tissue regions over time, visualized by violin plots of the number of unique genes per spot. The time points represent the approximate storage time after sample collection: ~1 month, ~6 months, and ~2 years. Replicates obtained for each time point are shown on the x -axis. The fill color of the violin plots indicates the applied protocol. For each time point, labels on the left side of the violin plots represent the average over all replicates. c RNA biotype content for the three datasets visualized as a pie chart. Proportions represent the UMI counts detected for each biotype. The targeted RRST data include protein coding, immunoglobulin, and T-cell receptor transcripts. d Mean-detection rate relationship in the mucosa for data collected at the three different time points. The y -axis shows log10-transformed average number of UMIs for each gene, and the x -axis shows the detection rate for each gene. The detection rate is defined as the fraction of spots where the gene is detected. e Spatial visualization of five enterocyte markers. Each row represents one selected tissue section from each time point with their corresponding H&E image in the leftmost column. Spot colors represent normalized gene expression.

Article Snippet: Here, authors present a genome-wide spatial gene expression profiling method called RNA Rescue Spatial Transcriptomics (RRST), designed for the analysis of moderate to low quality fresh frozen tissue samples and demonstrate its robustness on 7 different tissue types.

Techniques: Transformation Assay, Gene Expression

Journal: Nature Communications

Article Title: Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

doi: 10.1038/s41467-023-36071-5

Figure Lengend Snippet: a Average numbers of unique genes are highlighted by dashed lines for each protocol next to the violin plots. The y -axis represents log10-scaled counts. 1 tissue section was processed with the standard Visium protocol for P4 and P11, and 2 tissue sections for each timepoint (P4, P11) with the RRST protocol. b Following NNMF, Factors 12 and 2 associated with resting and proliferating chondrocytes. c Factors 1 and 11 associated with hypertrophic chondrocytes and primary spongiosa. d Factor 6 associated with the cruciate ligament. e Factor 7 associated with Perichondrium and periosteum. Spot colors represent the factor activity, i.e., the contribution of each spot to the factor. The spot opacity has been scaled by the factor activity scores, making spots with lower scores more transparent.

Article Snippet: Here, authors present a genome-wide spatial gene expression profiling method called RNA Rescue Spatial Transcriptomics (RRST), designed for the analysis of moderate to low quality fresh frozen tissue samples and demonstrate its robustness on 7 different tissue types.

Techniques: Activity Assay

Journal: Nature Communications

Article Title: Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

doi: 10.1038/s41467-023-36071-5

Figure Lengend Snippet: Overview of spatially resolved transcriptomics samples and filtering settings used in pre-processing steps

Article Snippet: Here, authors present a genome-wide spatial gene expression profiling method called RNA Rescue Spatial Transcriptomics (RRST), designed for the analysis of moderate to low quality fresh frozen tissue samples and demonstrate its robustness on 7 different tissue types.

Techniques: