Journal: bioRxiv

Article Title: Spatial Transcriptomics Reveals Inflammation and Trans-differentiation States of Acute Myeloid Leukemia in Extramedullary and Medullary Tissues

doi: 10.1101/2024.11.11.622999

Figure Lengend Snippet: (A) Schematic representation of the study workflow, visualized by BioRender ( https://biorender.com/ ). Concurrent Bone Marrow (BM) and Extramedullary (EM) (EM1 from skin and EM2 from lymph node) formalin-fixed paraffin-embedded samples from 2 newly diagnosed patients with acute myeloid leukemia (AML) (PT1 and PT2) were obtained. Each sample was sectioned and stained with hematoxylin and eosin (H&E). RNA integrity was evaluated using DV 200 measurements before library preparation. Visium ST assays v1 and CytAssist v2 were performed on each sample. After library preparation, cDNA traces were assessed, and samples meeting the criteria were sequenced for further analysis. (B) DV 200 values for BM1, BM2, EM1, and EM2 tissues, indicating RNA integrity before library preparation (C) Fragment size distribution percentages calculated within the 200-1000 bp range for DNA libraries prepared using v1 and v2 assays. BM samples showed significant improvement with v2 compared to v1. EM samples were within the acceptable range for both assays. (D) Spatial mapping by Seurat of captured oligonucleotides, genes, and mitochondrial gene percentages in BM and EM tissues. The v2 assay demonstrated higher oligonucleotide and gene capturing compared to v1, with mitochondrial gene percentages available only for v2. (E) Histological overlay of H&E-stained BM1 section with CytAssist image and spatial transcriptomics data, The image shows preserved structural integrity of bone regions, allowing detailed spatial analysis. The main tissue scale bar indicating 1 mm, while the zoomed-in panels, highlighting the boxed regions, denoting 400 µm.

Article Snippet: Unmixed Opal images were aligned with Visium Cytassist reference images by applying 15-25 common anchor points within the TissueAlign TM module of Visiopharm software (version 2024.07.1.16912 x64).

Techniques: Formalin-fixed Paraffin-Embedded, Staining

Journal: bioRxiv

Article Title: Spatial Transcriptomics Reveals Inflammation and Trans-differentiation States of Acute Myeloid Leukemia in Extramedullary and Medullary Tissues

doi: 10.1101/2024.11.11.622999

Figure Lengend Snippet: (A) Spatial map illustrating the alignment of Visium and OPAL-stained slides to obtain spot-level proteomic information at single-cell resolution. Dots and lines between OPAL and Visium H&E are representative to using common anchor point. (B) OPAL-stained images with phenotypic markers (CD33, CD68, CD71) and corresponding Visium spots exhibit similar spatial patterns. The top panel shows whole-slide pseudo-clor images with ROI squares, while left and right panels display zoomed OPAL and Visium images from identical locations. (C) Box and spatial plots of mfIHC intensities for phenotypic markers across ST-defined clusters, highlighting enrichment of leukemic and monocytic populations in cluster 3 and erythroid populations in cluster 2 at BM1. (D) OPAL and Visium images for functional markers confirm alignment of protein expression with ST data ( E ) Pearson correlation between IL-6 mfIHC intensities and the composite inflammation score demonstrates a positive association, with higher IL-6 levels observed in high-inflammation regions at PT1 samples ( F ) Box and spatial plots indicate increased CXCR4 intensity withing high-inflammatory niches in BM1, corroborating ST findings. Scale bars: 1 mm (whole-slide panels) and 100 µm (selected region panels).

Article Snippet: Unmixed Opal images were aligned with Visium Cytassist reference images by applying 15-25 common anchor points within the TissueAlign TM module of Visiopharm software (version 2024.07.1.16912 x64).

Techniques: Staining, Functional Assay, Expressing

Journal: bioRxiv

Article Title: Spatial Transcriptomics Reveals Inflammation and Trans-differentiation States of Acute Myeloid Leukemia in Extramedullary and Medullary Tissues

doi: 10.1101/2024.11.11.622999

Figure Lengend Snippet: (A) UMAP projection of 16,167 AML cells into differentiation states: primitive-like, granulocyte- monocyte progenitor (GMP)-like, erythroid-like and lymphoid-like, and committed-like ( B ) Spatial deconvolution maps of HLS-spots of BM1 showing primitive-like, GMP-like, and committed-like AML cells. (C) Spatial deconvolution maps of EM1 showing the same AML cell types. ( D, E) Violin plot showing the distribution of committed-like AML cells in BM1 ( D ), and EM1 ( E ) across inflammation class. ( F) Spatial map of Spatial Time calculation according to trabeculae overlaid with H&E image. ( G) Box plots showing deconvolution scores of primitive-like, GMP-like, and committed-like AML cells relative to their distance from bone in Visium data. Proximal is dark blue, distal is dark red. (H) GeoMx analysis of AML deconvolution in bone marrow regions from 3 AML patients. D: distal (dark red), P: proximal (dark blue), B:bone (white). Stacked bar plots represent cell type deconvolution within distal and proximal regions. The scale bar is 250µm. (I) Line graphs showing proportions of primitive-like and GMP-like cells relative to distance from bone. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, Wilcoxon rank sum test.

Article Snippet: Unmixed Opal images were aligned with Visium Cytassist reference images by applying 15-25 common anchor points within the TissueAlign TM module of Visiopharm software (version 2024.07.1.16912 x64).

Techniques:

Journal: bioRxiv

Article Title: Cell types in the mouse amygdala and their transcriptional response to fear conditioning

doi: 10.1101/2022.10.25.513733

Figure Lengend Snippet: a The main splits of amygdala taxonomy; neurotransmitter and glutamate transporter expression are regionally distinct along the amygdala anterior-posterior (a-p) axis, and between amygdala nuclei. Expression values calculated and normalized per gene across three a-p coronal sections of the Allen Mouse Brain Atlas, and segmented nuclei are colored by relative expression levels. Top panel shows the anatomical context of the sections below (approximate; exact matches were not available), from the Allen Mouse Brain (AMB) Reference Atlas. b-c Examples of inferred spatial distribution of scRNA-seq clusters, by correlating transcriptomic profiles with AMB ISH Atlas ( b ) or Visium spatial transcriptomics ( c ). The top rows show amygdala zoom-ins on four a-p (left-to-right) AMBA reference sections, with amygdala nuclei labelled. Bottom rows visualize correlation with the indicated cluster. For every cluster and every voxel (AMBA) or spot (Visium), a correlation coefficient heatmap is shown ( b dark, high; white, low; c red, high; grey, low). The top-scoring amygdala region is highlighted in bold. d Spatial correlation of all amygdala neuron clusters with the dissected amygdala nuclei, related or neighboring regions, and other higher-order grey matter structures. Regions are sorted by correlation similarity. Region names are colored according to the AMBA convention; regions that were not (fully) dissected are in italic; higher-order amygdala regions are in bold.

Article Snippet: Having a registered anatomical annotation for each 2D capture spot (see above, Spatial Transcriptomics (ST) Visium), we quantified spatial correlations for each scRNA-seq cluster per region, as presented in Suppl.

Techniques: Expressing

Journal: Genome Medicine

Article Title: SRT-Server: powering the analysis of spatial transcriptomic data

doi: 10.1186/s13073-024-01288-6

Figure Lengend Snippet: Analytic pipeline and results summary for the anterior mouse brain 10X Visium dataset. A The analytic pipeline built on the SRT-Server. The constructed analytic pipeline uses eight analytic modules, with the parameter settings for each module visualized on the panel. B The H&E staining image displays the general tissue structure of the anterior mouse brain. C A corresponding annotated brain tissue section from the Allen Brain Atlas. D A spatial scatter pie plot displays the inferred cell-type compositions across spatial locations from CARD in the DECON module. E The trajectory inferred by mature neuron whose proportion is estimated by cell2loction. F Fifteen detected spatial domains from SpatialPCA in the SDD module. G Bubble plot displays − log10( p -values) for different pathways from the ORA module ( y -axis). Six pathways are colored: GO biological process (red), GO molecular function (yellow), GO cellular component (blue), KEGG (grass-green), Reactome (grass-green), and Wikipathways (purple)

Article Snippet: Platform-specific data formats are available in the form of raw data produced by four commonly used SRT platforms, including 10 × Visium [ ], VIZGEN MERFISH [ ], seqFISH [ ], and Slide-SeqV2 [ ].

Techniques: Construct, Staining

Journal: Genome Medicine

Article Title: SRT-Server: powering the analysis of spatial transcriptomic data

doi: 10.1186/s13073-024-01288-6

Figure Lengend Snippet: Analytic pipeline and results summary for two tissues from the HER2 + BRCA 10 × Visium dataset. A The analytic pipeline built on the SRT-Server. The constructed analytic pipeline uses seven analytic modules, with the parameter settings for each module visualized on the panel. B The H&E staining image displays the general tissue structure. C A spatial scatter pie plot displays the inferred cell-type compositions across spatial locations from CARD in the DECON module. D 10 detected spatial domains from SpatialPCA in the SDD module. We used the clustering result in domain number = 10 to all the downstream analysis, which is not mentioned in the pipeline. E The pseudo-time inference result in the cancer epithelial is obtained from slingshot in the TRAJ module after DECON analysis. F The pseudo-time inference results are obtained from slingshot in the TRAJ module. The left two panels show the pseudo-time of different cell types on the two samples. The right two plots include arrows to display pseudo-time change on the tissue: the arrow point from tissue locations with low pseudo-time towards tissue locations with high pseudo-time

Article Snippet: Platform-specific data formats are available in the form of raw data produced by four commonly used SRT platforms, including 10 × Visium [ ], VIZGEN MERFISH [ ], seqFISH [ ], and Slide-SeqV2 [ ].

Techniques: Construct, Staining

Journal: GEN biotechnology

Article Title: Influence of Alzheimer’s disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex

doi: 10.1089/genbio.2023.0019

Figure Lengend Snippet: (A) Schematic of experimental design using Visium Spatial Proteogenomics (Visium-SPG) to investigate the impact of Aβ and pTau aggregates on the local microenvironment transcriptome in the post-mortem human brain. Human ITC blocks were acquired from 3 donors with AD and 1 age-matched neurotypical control. Tissue blocks were cryosectioned at 10μm to obtain 2–3 replicates per donor and sections were collected onto individual capture arrays of a Visium spatial gene expression slide, yielding a total of 3 gene expression experiments. The entire slide (4 tissue sections) was stained and scanned using multispectral imaging methods to detect Aβ and pTau immunofluorescence (IF) signals as well as autofluorescence. Following imaging, tissue sections were permeabilized and subjected to on-slide cDNA synthesis after which libraries were generated and sequenced. Transcriptomic data was aligned with the respective IF image data to generate gene expression maps of the local transcriptome with respect to Aβ plaques and pTau elements, including neurofibrillary tangles. (B) High magnification images show Aβ plaques (white triangles) and various neurofibrillary elements such as tangles (white arrowheads), neuropil threads (red arrowheads), and neuritic tau plaques (yellow arrowheads). Lipofuscin (cyan) was identified through spectral unmixing and pixels confounded with this autofluorescent signal were excluded from analysis, scale bar, 20μm. (C) ITC tissue block from Br3880 (left) and corresponding spotplots (right) from the Visium data show gene expression of MOBP and SNAP25, which demarcates the border between gray matter (GM) and white matter (WM), scale bar, 1mm. Color scale indicates spot-level gene expression in logcounts. (D) Image processing and quantification of Aβ and pTau per Visium spot. Aβ and pTau signals were thresholded in their single IF channels for segmentation against autofluorescence background, including lipofuscin. Thresholded Aβ and pTau signals were aligned to the gene expression map of the same tissue section from Br3880 and quantified as the proportion of number of pixels per Visium spot, which is visualized in a spotplot, scale bar, 1mm.

Article Snippet: To better understand molecular signaling in the tissue environment local to pathology in the human brain during late-stage AD, we utilized spatial profiling coupled with multiplex immunofluorescence using the 10x Genomics Visium Spatial Proteogenomics platform (Visium-SPG) to generate a proteomic-based, spatially-resolved, transcriptome-scale map of the human inferior temporal cortex (ITC), a region which displays reduced cortical thickness in AD.

Techniques: Control, Gene Expression, Staining, Imaging, Immunofluorescence, cDNA Synthesis, Generated, Blocking Assay

Journal: GEN biotechnology

Article Title: Influence of Alzheimer’s disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex

doi: 10.1089/genbio.2023.0019

Figure Lengend Snippet: (A) Flowchart of experimental design and data analysis. Human ITC tissues from 3 original AD donors plus additional male AD donor (Br8549) were subjected to multiplexed staining using RNAscope smFISH combined with immunofluorescence (FISH-IF) to detect genes of interest (GOIs) and Aβ plaques. Images were analyzed with HALO image analysis software to assess spatial relationships between Aβ and cells expressing GOI. The FISH-IF module of HALO was used for image segmentation and quantification of Aβ and GOIs. The proximity analysis module was used to determine a distance between Aβ and cells expressing or not expressing GOIs. The outputs of the two modules were integrated to measure the gene expression of GOIs within a predefined proximity of Aβ at cellular resolution. (B) Schematic describing proximity analysis. An Aβ-associated microenvironment was demarcated by approximating the Visium spot grid-line system in which the center of a single Visium spot is 127.5μm away from its neighboring spot. This distance was further subdivided into 6 evenly spaced intervals, resulting in a total of 7 bins to finely resolve the spatial gene expression gradients of GOIs. The proximity between Aβ and nearby cells expressing and not expressing GOIs was measured and used to classify into the 7 bins for quantifying the average GOI gene expression. (C) RNA-protein co-detection of Aβ and IDI1, C3, NINJ1, PPP3CA reveals the spatial distribution patterns of Aβ (cyan) and GOIs (magenta) at lower (Top, scale bar: 50μm) and higher magnifications (Bottom, scale bar: 12.5μm). Proximity lines indicate the distance between Aβ and nearby cells expressing GOIs (max: 127.5μm). (D) Bar plots show quantification of gene expression levels for GOIs in Figure 3C across 7 consecutive bins representing increased distance from Aβ, as modeled in Figure 3B. Gene expression levels were determined with log2 (X+1) transformation where X represents the counts of puncta in a single cell for a given GOI. Data are mean ± SEM. The first bin was compared to all the rest by default for statistical tests (Kruskal-Wallis test, *p<0.05, &p<0.005, and #p<0.0001). The bracket denotes statistical testing between two specified bins. Violin plots are provided in Figure S18C describing the cellular distribution and numbers counted for each bin.

Article Snippet: To better understand molecular signaling in the tissue environment local to pathology in the human brain during late-stage AD, we utilized spatial profiling coupled with multiplex immunofluorescence using the 10x Genomics Visium Spatial Proteogenomics platform (Visium-SPG) to generate a proteomic-based, spatially-resolved, transcriptome-scale map of the human inferior temporal cortex (ITC), a region which displays reduced cortical thickness in AD.

Techniques: Staining, RNAscope, Immunofluorescence, Software, Expressing, Gene Expression, Transformation Assay