highly multiplexed imaging platforms like codex  (Vectra Laboratories)

Journal: Nature Communications

Article Title: Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma

doi: 10.1038/s41467-021-26974-6

Figure Lengend Snippet: a SpatialScore schematic. The SpatialScore is calculated by taking the ratio of the physical distance between each CD4 + T cell and its nearest tumor cell (distance “right”) relative to its nearest Treg (distance “left”). [1] A lower SpatialScore (i.e., CD4 + T cells closer to tumor cells than Tregs) suggests increased T cell effector activity. [2] A higher spatial score (i.e., CD4 + T cells closer to Tregs than tumor cells) suggests increased T cell suppression. b – c SpatialScore calculated per cell for all CD4 + T cells ( b ) and PD-1 + CD4 + T cells ( c ) across patient groups (mean ± s.e.m.). P values calculated with a linear mixed-effect model taking a patient identifier as a random effect. d GZMB protein expression on PD-1 + CD4 + T cells by CODEX per tissue microarray spot (mean fluorescence intensity (arbitrary units, a.u.), red bar). P values calculated with a linear mixed-effect model with Bonferroni’s corrections for multiple comparisons. e CODEX images showing contact between a tumor cell (cross) and GZMB-expressing PD-1 + CD4 + T cell (arrow) in responder patient 13 post-treatment. Scale bars, 10 µm. f Cytotoxicity gene scores, computed on bulk RNA-seq data, per tissue microarray spot. Boxes, median ± upper and lower quartiles; whiskers, 1.5x IQR. P values calculated with a linear mixed-effect model with Bonferroni’s corrections for multiple comparisons. g – h Pre- to post-treatment changes in tumor therapy resistance gene scores, computed on bulk RNA-seq data, per patient in responders ( g ) and nonresponders ( h ). Boxes, median ± upper and lower quartiles; whiskers, 1.5x IQR. P values were calculated by two-sided Wilcoxon’s signed-rank tests. i Ki-67 + tumor cell frequencies per tissue microarray spot (mean, red bar). P values calculated with a linear mixed-effect model with Bonferroni’s corrections for multiple comparisons. j SpatialScore calculated from Vectra mIHC data per cell for all PD-1 + CD4 + T cells (mean ± s.e.m.). P values calculated with a linear mixed-effect model taking a patient identifier as a random effect. k Vectra mIHC images (left panels) and corresponding spatial plots (right panels)from responder patient 13 (R) and nonresponder patient 14 (NR) pretreatment. Scale bars, 20 µm. l SpatialScore calculated from Vectra mIHC data per patient in responders and nonresponders pretreatment(mean, red bar). P value calculated by a two-sided Wilcoxon’s rank-sum test, with no adjustments for multiple hypotheses. Source data are provided as a Source Data file.

Article Snippet: Collectively, these results show that findings identified by highly multiplexed imaging platforms like CODEX can be translated to more clinically accessible platforms like Vectra.

Techniques: Activity Assay, Expressing, Microarray, Fluorescence, RNA Sequencing

Journal: Nature Communications

Article Title: Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma

doi: 10.1038/s41467-021-26974-6

Figure Lengend Snippet: a Seven genes from bulk RNAseq data predictive of the SpatialScore . b Normalized bulk CXCL13 gene expression per tissue microarray spot. Boxes, median ± upper and lower quartiles; whiskers, 1.5x IQR. P values calculated with a linear mixed-effect model with Bonferroni’s corrections for multiple comparisons. c CXCL13 protein expression by IHC per tissue microarray spot (mean, red bar). P values calculated with a linear mixed-effect model with Bonferroni’s corrections for multiple comparisons. d Representative CXCL13 IHC images from responder patient 9 (left panels) and nonresponder patient 14 (right panels). Scale bars, 20 µm. e – f CXCL13 expression in single-cell transcriptomes from CTCL skin tumors (Gaydosik et al.) . e Normalized expression of CXCL13 in single cells; excluded cells with CXCL13 log1p normalized read counts < 0.5. Boxes, median ± upper and lower quartiles; whiskers, 1.5x IQR. f Proportion of CXCL13-expressing cells per cell type. g CIBERSORTx workflow schematic. A CSx deconvolution signature matrix was generated from single-cell transcriptomes (Gaydosik et al.) (left) and applied to CTCL bulk transcriptomes obtained with laser-capture microdissection (LCM) and Smart-3Seq (right) to enumerate cell type fractions and resolve gene expression profiles. h Heatmap correlation of CSx-resolved and CODEX-identified cell type frequencies; Spearman coefficients are on the diagonal. i – j Differential expression of CSx-resolved tumor cell genes in responders ( j ) and nonresponders ( k ) pre- and post-treatment. P values calculated with a linear mixed-effect model with Benjamini-Hochberg correction (significantly different genes ( p < 0.1), red; CXCL13 highlighted yellow). k Vectra mIHC image (top left), corresponding tumor cell depiction (top right), corresponding CXCL13 IHC image (bottom left), and corresponding overlay image of CXCL13 staining and tumor cells (bottom right) in responder patient 9 post-treatment. Scale bars, 20 µm. l Normalized CSx-resolved CXCL13 expression in tumor cells per tissue microarray spot. Boxes, median ± upper and lower quartiles; whiskers, 1.5x IQR. P values calculated with a linear mixed-effect model with Bonferroni’s corrections for multiple comparisons. m – n Pre- to post-treatment changes in normalized CXCL13 gene expression from CSx-resolved tumor genes per patient in responders ( m ) and nonresponders ( n ). Boxes, median ± upper and lower quartiles; whiskers, 1.5x IQR. P values calculated by two-sided Wilcoxon’s signed-rank tests. o Correlation of CSx-resolved tumor cell CXCL13 expression and bulk CXCR5 expression per tissue microarray spot. Data evaluated with two-sided Spearman test. Source data are provided as a Source Data file.

Article Snippet: Collectively, these results show that findings identified by highly multiplexed imaging platforms like CODEX can be translated to more clinically accessible platforms like Vectra.

Techniques: Gene Expression, Microarray, Expressing, Generated, Laser Capture Microdissection, Quantitative Proteomics, Staining