date_published

n_cells/bins/spots

spot_diameter_um

spot_distance_um

developmental_stage

section_thickness_um

downstream

state/province

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

https://doi.org/10.1126/science.aaf2403

Mus musculus

olfactory bulb

However, the spot locations and gene counts can be obtained from the SpatialDE GitHub repo, though no link or accession is provided in the paper. Not all spots on the slides were used

Bowtie2 alignment

Transcript count distribution; comparison with smFISH and laser microdissection; DE bewtween different regions; tSNE and clustering of spots

Karolinska Institute

Department of Cell and Molecular Biology

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

https://doi.org/10.1126/science.aaf2403

Homo sapiens

invasive ductal cancer

Karolinska Institute

Department of Cell and Molecular Biology

An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries

Scientific Reports

https://doi.org/10.1038/srep37137

Homo sapiens

Gingival tissue biopsy

Comparison between manual and automated protocols and between replica

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

left ventricle (individual 1)

STAR 2.4.2a alignment with ST pipeline

Correlation between consecutive sections; PCA of samples; tSNE; DE on regions; comparison with bulk data for fetal marker genes

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

left ventricle (individual 1)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

right atrial appendage (individual 1)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

right atrial appendage (individual 1)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

left ventricle (individual 2)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

left ventricle (individual 2)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

left ventricle (individual 3)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

left ventricle (individual 3)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

right atrial appendage (individual 3)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Scientific Reports

https://doi.org/10.1038/s41598-017-13462-5

Homo sapiens

right atrial appendage (individual 3)

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Gene expression profiling of periodontitis-affected gingival tissue by spatial transcriptomics

Scientific Reports

https://doi.org/10.1038/s41598-018-27627-3

Homo sapiens

Gingival tissue biopsy

Clustering and tSNE of spots; DE between inflamed and non-inflamed regions; GO term enrichment in inflamed region

Karolinska Institute

Department of Dental Medicine, Division of Periodontology

Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity

Nature Communications

https://doi.org/10.1038/s41467-018-04724-5

Homo sapiens

cancerous prostate after radical prostatectomy

12 samples from patient 1, also have samples from patients 2 and 3

Factor analysis (can be extended for spatial field), PCA of spots; clustering of factors; DE between cancer and periphery; GSEA of those DE genes; DE between reactive and normal stroma and then GSEA; tSNE of spots

https://github.com/maaskola/spatial-transcriptome-deconvolution

Royal Institute of Technology

Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health

Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma

Cancer Research

https://doi.org/10.1158/0008-5472.CAN-18-0747

Homo sapiens

Lymph node metastases from four patients diagnosed with stage III melanoma

Factor analysis as in the previous entry; PCA of samples; tSNE of spots; PCA of spots; clustering of spots

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

https://doi.org/10.1126/science.aaw1219

Mus musculus

Data available here: https://singlecell.broadinstitute.org/single_cell/study/SCP354/slide-seq-study#study-download

tSNE; NMFreg to reconstruct expression; find genes with spatially non-random distribution; gene correlation (They think it's spatial, but I don't think so for it would be the same if they just did correlation with gene vectors); GSEA of genes correlated with Vim, Gfap, and Ctsd

https://github.com/broadchenf/Slideseq

MATLAB; R; C; Python

Department of Physics

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

https://doi.org/10.1126/science.aaw1219

Mus musculus

https://github.com/broadchenf/Slideseq

MATLAB; R; C; Python

Department of Physics

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

https://doi.org/10.1126/science.aaw1219

Mus musculus

olfactory bulb

https://github.com/broadchenf/Slideseq

MATLAB; R; C; Python

Department of Physics

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

https://doi.org/10.1126/science.aaw1219

Mus musculus

https://github.com/broadchenf/Slideseq

MATLAB; R; C; Python

Department of Physics

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

https://doi.org/10.1126/science.aaw1219

Mus musculus

https://github.com/broadchenf/Slideseq

MATLAB; R; C; Python

Department of Physics

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

https://doi.org/10.1126/science.aaw1219

Homo sapiens

https://github.com/broadchenf/Slideseq

MATLAB; R; C; Python

Department of Physics

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

https://doi.org/10.1126/science.aaw1219

Mus musculus

Injured cortex

https://github.com/broadchenf/Slideseq

MATLAB; R; C; Python

Department of Physics

Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis

https://doi.org/10.1126/science.aav9776

Homo sapiens

lumbar spinal cord tissue sections

Hierarchical generative modeling that does take spatial autocorrelation into account; this is used for DE between anatomical regions and between healthy and ALS mice; gene coexpression and GSEA of coexpressed genes

https://zenodo.org/record/2566612#.XqO6hFNKifU

New York Genome Center

Center for Genomics of Neurodegenerative Disease

Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis

https://doi.org/10.1126/science.aav9776

Homo sapiens

lumbar spinal cord tissue sections

https://zenodo.org/record/2566612#.XqO6hFNKifU

New York Genome Center

Center for Genomics of Neurodegenerative Disease

High-definition spatial transcriptomics for in situ tissue profiling

Nature Methods

https://doi.org/10.1038/s41592-019-0548-y

Mus musculus

olfactory bulb

Processed data here: https://singlecell.broadinstitute.org/single_cell/study/SCP420/hdst

Binning the hexagonal wells; DE between anatomical regions; multinomial naïve Bayes classifier to integrate with scRNA-seq data for cell type annotation; intronic vs exonic reads in nuclei after nuclear segmentation

https://github.com/klarman-cell-observatory/hdst

MATLAB; R; Python

Broad Institute

Klarman Cell Observatory

High-definition spatial transcriptomics for in situ tissue profiling

Nature Methods

https://doi.org/10.1038/s41592-019-0548-y

Homo sapiens

histological grade 3 breast HER2+ cancer

H&E annotation; DE; cell type inference

https://github.com/klarman-cell-observatory/hdst

MATLAB; R; Python

Broad Institute

Klarman Cell Observatory

Spatially resolved transcriptome profiling in model plant species

Nature Plants

https://doi.org/10.1038/nplants.2017.61

Arabidopsis thaliana

inflorescence meristem

Gene count matrices were posted on spatialtranscriptomicsresearch.org, which is no longer functional

Comparison with microarray; clustering and tSNE of spots; linear model accounting for technical replica, spots, and tissues for DE for tissue domains, with permutation for FDR; network enrichment analysis for DE genes

https://github.com/stefaniagiacomello/Spatial-transcriptomics-data-analysis-in-plants

Royal Institute of Technology

Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health

Spatially resolved transcriptome profiling in model plant species

Nature Plants

https://doi.org/10.1038/nplants.2017.61

Arabidopsis thaliana

Populus tremula developing and dormant leaf buds

PCA of spots; DE between developing and dormant leaf buds; GO term enrichment of DE genes; OPLS to confirm separation of developing and dormant

https://github.com/stefaniagiacomello/Spatial-transcriptomics-data-analysis-in-plants

Royal Institute of Technology

Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health

Spatially resolved transcriptome profiling in model plant species

Nature Plants

https://doi.org/10.1038/nplants.2017.61

Arabidopsis thaliana

Picea abies female cones

PCA of spots

https://github.com/stefaniagiacomello/Spatial-transcriptomics-data-analysis-in-plants

Royal Institute of Technology

Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health

Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics

Scientific Reports

https://doi.org/10.1038/s41598-019-55441-y

Homo sapiens

PRJNA580481

3 patients for each condition, and 3 sections from each patient; gene count matrix was posted on spatialresearch.org, which is no longer functional

DE between RA and SpA; spot clustering; annotation of infiltrate regions; GO enrichment of DE genes; cell type assignment using xCell

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart

https://doi.org/10.1016/j.cell.2019.11.025

Homo sapiens

4.5-5, 6.5, 9 PCW

Again, the matrix was posted on spatialresearch.org, which is down. However, the data can still be visualized in a Shiny app

Gene coexpression between samples; clustering spots; DE between anatomical regions; GSEA of the DE genes

MATLAB; R; C++

Royal Institute of Technology

Department of Gene Technology, Science for Life Laboratory

Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas

Nature Biotechnology

https://doi.org/10.1038/s41587-019-0392-8

Homo sapiens

Pancreatic duct adenocarcinoma

PCA and clustering of spots; Multimodal intersection analysis (MIA), which is to see whether intersection of lack of it between cell type markers and region markers is significant for cell type enrichment in regions and deconvolution; reanalysis of the 2018 melanoma ST dataset with MIA

NYU Langone Health

Institute for Computational Medicine

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Nature Neuroscience

https://doi.org/10.1038/s41593-020-00787-0

Homo sapiens

human postmortem DLPFC (neurotypical)

Data is available on ExperimentHub

Assign spots to layers; pseudobulking layers; clustering pseudobulked data; DE for each layer with linear mixed-effects modeling; testing for enrichment of published layer-enriched genes; layer registration of snRNA-seq data with the layer enriched genes; disease gene sets; DE genes for diseases in each layer; TWAS; SpatialDE; HVG; unsupervized and semi-supervized classification of spots into patterns with SpatialDE genes and HVG

https://github.com/LieberInstitute/HumanPilot; https://github.com/LieberInstitute/spatialLIBD

MATLAB; R; Python

Johns Hopkins Medical Campus

Lieber Institute for Brain Development

Spatial transcriptomics identifies spatially dysregulated expression of GRM3 and USP47 in amyotrophic lateral sclerosis

Neuropathology and Applied Neurobiology

https://doi.org/10.1111/nan.12597

Homo sapiens

Post mortem ALS cerebellum

DE between ALS and control granule cell layers; GO analysis of DE genes

University of Edinburgh

Centre for Clinical Brain Sciences

Spatial transcriptomics identifies spatially dysregulated expression of GRM3 and USP47 in amyotrophic lateral sclerosis

Neuropathology and Applied Neurobiology

https://doi.org/10.1111/nan.12597

Homo sapiens

Post mortem control cerebellum

University of Edinburgh

Centre for Clinical Brain Sciences

Transcriptional output, cell types densities and normalization in spatial transcriptomics

Journal of Molecular Cell Biology

https://doi.org/10.1093/jmcb/mjaa028

Homo sapiens

BRAF V600E-mutated papillary thyroid cancer

thyroid_gland

Total reads per spot is associated with number of cells in the spot; raw counts vs. normalization with DCA; correlation between genes in raw count and normalized data

https://github.com/vdet/st-normalization

Université Libre de Bruxelles

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2

Nature Biotechnology

https://doi.org/10.1038/s41587-020-0739-1

Mus musculus

CA1 neuro soma and dendrites are projected to 1D, and genes are clustered by pattern on that projection; GO enrichment in each gene cluster; DE between soma and dendrites

R; Python; MATLAB

Broad Institute

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2

Nature Biotechnology

https://doi.org/10.1038/s41587-020-0739-1

Mus musculus

Clustering of beads; scVelo; Monocle 3; "spatial" variable genes just like in the first slide-seq paper, then correlated to the spatial latent time axis; clustering genes associated with developmental disorders according to spatial latent time patterns; GO enrichment in clusters

R; Python; MATLAB

Broad Institute

High-Spatial-Resolution Multi-Omics Atlas Sequencing of Mouse Embryos via Deterministic Barcoding in Tissue

https://doi.org/10.1016/j.cell.2020.10.026

Mus musculus

Clustering of the locations; correlation between proteins and transcripts; average expression in each annotated region; SpatialDE

Yale University

Department of Biomedical Engineering

High-Spatial-Resolution Multi-Omics Atlas Sequencing of Mouse Embryos via Deterministic Barcoding in Tissue

https://doi.org/10.1016/j.cell.2020.10.026

Mus musculus

Spatial barcode grid is created by two orthogonal sets of microfluidics channels that introduce barcodes. Resolution depends on width of the channels. 10um resolution can be achieved, but 50um was used for this sample. It seems from their figures that there's quite a bit of artefact in spatial reconstruction from the barcodes.

Use protein to define tissue regions and then DE and GO analysis

Yale University

Department of Biomedical Engineering

High-Spatial-Resolution Multi-Omics Atlas Sequencing of Mouse Embryos via Deterministic Barcoding in Tissue

https://doi.org/10.1016/j.cell.2020.10.026

Mus musculus

Yale University

Department of Biomedical Engineering

Automation of Spatial Transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues

BMC Genomics

https://doi.org/10.1186/s12864-020-6631-z

Mus musculus

olfactory bulb

PRJNA598447

Royal Institute of Technology

Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health

Lineage recording reveals dynamics of cerebral organoid regionalization

https://doi.org/10.1101/2020.06.19.162032

Homo sapiens

iPSC derived cerebral organoid

Code repo is not available yet

Cell type deconvolution with CIBERSORTx

https://github.com/quadbiolab/iTracer

Department of Biosystems Science and Engineering

Integrating Spatial Gene Expression and Breast Tumour Morphology via Deep Learning

Nature Biomedical Engineering

https://doi.org/10.1038/s41551-020-0578-x

Homo sapiens

breast tumors

I also wonder what if they used a different histological stain or if there are multiple stains from serial sections. Anyway, H&E is the most common. They said the data is on spatialtranscriptomicsresearch.org, which no longer works.

https://github.com/bryanhe/ST-Net

Stanford University

Department of Computer Science

Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma

https://doi.org/10.1016/j.cell.2020.05.039

Homo sapiens

cutaneous squamous cell carcinoma

The analyses in this paper are REALLY in depth and thorough. This paper makes me feel so stupid when I have little idea what those genes are for. Though I've been using Seurat for years, I didn't know that the function AddModuleScore existed until I read this paper. It seems that this study has been going on since as early as 2018, since part of the analyses were done with Seurat v2 and the scRNA-seq data is 10xv2, though some later parts were done with Seurat v3. They did use MATLAB and Python (DeepCell for cell segmentation, finally I saw someone using it!) for MIBI, which is for spatial proteomics and is thus outside the scope of this museum, so I'm not putting MATLAB and Python here. Unfortunately, the analysis scripts are "upon request" :( but they did write an R package for this paper that does EDA for ST. I'm not sure if there is a dominant package for ST annd Visium EDA like Seurat for scRNA-seq yet, and I have already seen several popping up. I wonder what makes any of them become dominant in the near future.

ICA; Clustering; TSK score of each cluster for each patient; TSK-stromal signature score, the scores are calculated by Seurat's AddModuleScore; how many spots in each cluster are in the leading edge; GO enrichment of DE genes for non-TSK leading edge clusters; overlap correlation matrix between clusters among patients; number of adjacent spots from each cluster, with permutation test; inspecting DC and T cell marker genes and expression of chemokines and receptors; spatial gene correlation between Treg and CD8 T cell genes and FOXP3; integrating scRNA-seq and ST datasets; NicheNet to prioritize ligand receptor pairs

https://github.com/jbergenstrahle/STUtility

Stanford University School of Medicine

Program in Epithelial Biology

Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma

https://doi.org/10.1016/j.cell.2020.05.039

Homo sapiens

cutaneous squamous cell carcinoma

There seems to be more and more interest in spatial transcriptomics data analysis recently, especially ST and Visium. OK, Seurat's spatial part isn't that in depth. But now we've got several attempts at general EDA packages dedicated to spatial data: Giotto, Spaniel, and now STUtility. I'd better hurry up and finish museumst and start writing Voyager and make it even cooler, and I'll overcomplicated the hell out of it.

https://github.com/jbergenstrahle/STUtility

Stanford University School of Medicine

Program in Epithelial Biology

Molecular atlas of the adult mouse brain

https://doi.org/10.1126/sciadv.abb3446

Mus musculus

It seems that gene expression does a pretty good job in defining spatial region here. And this paper did not use cool geospatial statistics. I wonder whether adapting geospatial satistics would be helpful at all. Anyway, I won't know until I look into it, and I need to sit down and more clearly delineate the questions to ask. Data available here: https://www.molecularatlas.org/

ICA; clustering; tSNE; UMAP; hierarchical clustering of the molecular clusters; SVM to transform molecular clusters into continuous 3D volumes; neural network trained on ST data to predict spatial origin of scRNA-seq data from ABA; normalized mutual information index between whole atlas and reduced list of genes on independent components and SVM model weights to find genes that best capture global spatial signal; GO enrichment among the 266 representative genes

https://github.com/cantin-ortiz/molecular-atlas

Karolinska Institute

Department of Neuroscience

Genome-wide spatial expression profiling in formalin-fixed tissues

Cell Genomics

https://doi.org/10.1016/j.xgen.2021.100065

Mus musculus

brain coronal plate (FFPE)

Harmony integration of FFPE and fresh frozen tissue. As usual, UMAP, clustering, DE. Also integration with scRNA-seq dataset with stereoscope for cell type deconvolution. Overlap of DE genes across conditions.

Royal Institute of Technology

Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health

Genome-wide spatial expression profiling in formalin-fixed tissues

Cell Genomics

https://doi.org/10.1016/j.xgen.2021.100065

Homo sapiens

ovarian carcinosarcoma metastasis to the omentum (FFPE)

NMF, get top contributor genes for each factor, which are used for pathway analysis.

Royal Institute of Technology

Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health

Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer's Disease

https://doi.org/10.1016/j.cell.2020.06.038

Mus musculus

3, 6, 12, and 18 months

Again, the sections have been aligned to ABA's brain regions. Again, the "prequel" approach is used. But unfortunately, ST spots are too large for Drosophila and early stage chicken embryos. Also for those model systems, sectioning can be problematic, and smFISH and ISS approaches are typically applied to sections rather than whole mount.

tSNE; GO enrichment over age and genotype; WGCNA gene co-expression modules; WGCNA on the PIG genes again; OLIG modules over age and genotype and amyloid beta levels

https://www.alzmap.org/

University of Leuven

Department of Neurosciences

Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer's Disease

https://doi.org/10.1016/j.cell.2020.06.038

Mus musculus

3, 6, 12, and 18 months

Data and analyses are supposed to be available on https://www.alzmap.org/, but that website doesn't work, at least on my computer.

https://www.alzmap.org/

University of Leuven

Department of Neurosciences

A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain

Nature Communications

https://doi.org/10.1038/s41467-020-20343-5

Mus musculus

C57BL/6NTac

hippocampus and prefrontal cortex

Visium protocol was modified for Oxford Nanopore long read sequencing. While I haven't figured out how to get the fasstq files and the gene count matrices, there's a shiny app that visualizes the data: https://isoformatlas.com/

Differential isoform test

https://github.com/noush-joglekar/scisorseqr

R; Python; shell; awk

Weill Cornell Medicine

Brain and Mind Research Institute and Center for Neurogenetics

Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram

https://doi.org/10.1101/2020.08.29.272831

Mus musculus

Data and code repo are not (yet) in the paper.

Broad Institute of MIT and Harvard

The spatial landscape of gene expression isoforms in tissue sections

Nucleic Acids Resech

https://doi.org/10.1093/nar/gkad169

Mus musculus

olfactory bulb

Again, both Nanopore long read sequencing and 3' based sequencing were performed to get isoforms. I'm not sure if this should be called something other than Visium as the authors did.

https://github.com/ucagenomix/SiT

Sophia Antipolis

Université Côte d’Azur

The spatial landscape of gene expression isoforms in tissue sections

Nucleic Acids Resech

https://doi.org/10.1093/nar/gkad169

Mus musculus

left hemisphere

Data visualization here: https://www.isomics.eu

https://github.com/ucagenomix/SiT

Sophia Antipolis

Université Côte d’Azur

The Stress-Like Cancer Cell State Is a Consistent Component of Tumorigenesis

https://doi.org/10.1016/j.cels.2020.08.018

minicoopr mitfa-BRAFV600E;p53−/−;mitfa−/−

melanoma tumors

https://github.com/MaayanBaron/sc_melanoma_Baron2020

NYU Grossman School of Medicine

Institute for Computational Medicine

Spatially resolved and multiplexed MicroRNA quantification from tissue using nanoliter well arrays

Microsystems & Nanoengineering

https://dx.doi.org/10.1038%2Fs41378-020-0169-8

miRNA nanowell

Mus musculus

K-rasLSL-G12D/+; p53fl/fl

non small cell lung tumors (FFPE)

Department of Chemical Engineering

Repopulating Microglia Promote Brain Repair in an IL-6-Dependent Manner

https://doi.org/10.1016/j.cell.2020.02.013

Mus musculus

dentate gyrus (TBI, with or without microglia repopulation)

E-MTAB-8768

The GitHub repo is unavailable

https://github.com/BiomedicalMachineLearning/stLearn/TBI

The University of Queensland

School of Biomedical Sciences, Faculty of Medicine

A robust experimental and computational analysis framework at multiple resolutions, modalities and coverages

Frontiers in Immunology

https://doi.org/10.3389/fimmu.2022.911873

Homo sapiens

squamous cell carcinoma

https://github.com/BiomedicalMachineLearning/SkinSpatial

Python; QuPath

The University of Queensland

Institute for Molecular Bioscience

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

https://doi.org/10.1016/j.isci.2020.101556

Mus musculus

Clustering the spots; DE between clusters and genotypes; DE between regions

https://github.com/jfnavarro/AD_POLB_ST

Royal Institute of Technology

Science for Life Laboratory, Department of Gene Technology

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

https://doi.org/10.1016/j.isci.2020.101556

Mus musculus

https://github.com/jfnavarro/AD_POLB_ST

Royal Institute of Technology

Science for Life Laboratory, Department of Gene Technology

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

https://doi.org/10.1016/j.isci.2020.101556

Mus musculus

https://github.com/jfnavarro/AD_POLB_ST

Royal Institute of Technology

Science for Life Laboratory, Department of Gene Technology

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

https://doi.org/10.1016/j.isci.2020.101556

Mus musculus

https://github.com/jfnavarro/AD_POLB_ST

Royal Institute of Technology

Science for Life Laboratory, Department of Gene Technology

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

https://doi.org/10.1016/j.isci.2020.101556

Mus musculus

olfactory bulb

https://github.com/jfnavarro/AD_POLB_ST

Royal Institute of Technology

Science for Life Laboratory, Department of Gene Technology

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

https://doi.org/10.1016/j.isci.2020.101556

Mus musculus

olfactory bulb

https://github.com/jfnavarro/AD_POLB_ST

Royal Institute of Technology

Science for Life Laboratory, Department of Gene Technology

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

https://doi.org/10.1016/j.isci.2020.101556

Mus musculus

olfactory bulb

https://github.com/jfnavarro/AD_POLB_ST

Royal Institute of Technology

Science for Life Laboratory, Department of Gene Technology

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

https://doi.org/10.1016/j.isci.2020.101556

Mus musculus

olfactory bulb

https://github.com/jfnavarro/AD_POLB_ST

Royal Institute of Technology

Science for Life Laboratory, Department of Gene Technology

Dissecting Mammalian Spermatogenesis Using Spatial Transcriptomics

https://doi.org/10.1016/j.celrep.2021.109915

Mus musculus

7 to 18 weeks

PRJNA668433

Monocle pseudotime of beads; NMFreg cell type deconvolution; GO enrichment of spatially variable genes; clustering beads

https://github.com/thechenlab/Testis_Slide-seq

R; Python; MATLAB

Broad Institute of MIT and Harvard

Dissecting Mammalian Spermatogenesis Using Spatial Transcriptomics

https://doi.org/10.1016/j.celrep.2021.109915

Homo sapiens

PRJNA668433

https://github.com/thechenlab/Testis_Slide-seq

R; Python; MATLAB

Broad Institute of MIT and Harvard

SM-Omics is an automated platform for high-throughput spatial multi-omics

Nature Communications

https://doi.org/10.1038/s41467-022-28445-y

Mus musculus

olfactory bulb

Basically ST but with antibody coupled oligos to quantify proteins. Repo is not provided, but I can tell that they used base R, ggplot2, and matplotlib from the plot styles.

Broad Institute of MIT and Harvard

SM-Omics is an automated platform for high-throughput spatial multi-omics

Nature Communications

https://doi.org/10.1038/s41467-022-28445-y

Mus musculus

olfactory bulb

Broad Institute of MIT and Harvard

SM-Omics is an automated platform for high-throughput spatial multi-omics

Nature Communications

https://doi.org/10.1038/s41467-022-28445-y

Mus musculus

Broad Institute of MIT and Harvard

SM-Omics is an automated platform for high-throughput spatial multi-omics

Nature Communications

https://doi.org/10.1038/s41467-022-28445-y

Mus musculus

Broad Institute of MIT and Harvard

SM-Omics is an automated platform for high-throughput spatial multi-omics

Nature Communications

https://doi.org/10.1038/s41467-022-28445-y

Mus musculus

Broad Institute of MIT and Harvard

SM-Omics is an automated platform for high-throughput spatial multi-omics

Nature Communications

https://doi.org/10.1038/s41467-022-28445-y

Mus musculus

colorectal cancerr

Broad Institute of MIT and Harvard

SM-Omics is an automated platform for high-throughput spatial multi-omics

Nature Communications

https://doi.org/10.1038/s41467-022-28445-y

Mus musculus

Broad Institute of MIT and Harvard

Inferring spatially transient gene expression pattern from spatial transcriptomic studies

https://doi.org/10.1101/2020.10.20.346544

Homo sapiens

https://github.com/theMILOlab/SPATA

Universität Freiburg

Microenvironment and Immunology Research Laboratory, Medical Center

Inferring spatially transient gene expression pattern from spatial transcriptomic studies

https://doi.org/10.1101/2020.10.20.346544

Homo sapiens

https://github.com/theMILOlab/SPATA

Universität Freiburg

Microenvironment and Immunology Research Laboratory, Medical Center

Mapping endothelial-cell diversity in cerebral cavernous malformations at single-cell resolution

https://doi.org/10.7554/eLife.61413

Mus musculus

Cdh5(PAC)-Cre-ERT2/Ccm3f/f/Cldn5(BAC)-GFP (tamoxifen)

cerebellum with cerebral cavernous malformation

Inspecting marker genes and TF in the Visium data

FIRC Institute of Molecular Oncology Foundation

Vascular Biology Unit

Mapping endothelial-cell diversity in cerebral cavernous malformations at single-cell resolution

https://doi.org/10.7554/eLife.61413

Mus musculus

Cdh5(PAC)-Cre-ERT2/Ccm3f/f/Cldn5(BAC)-GFP (wt)

FIRC Institute of Molecular Oncology Foundation

Vascular Biology Unit

Spatial transcriptomics reveals the architecture of the tumor/microenvironment interface

https://doi.org/10.1101/2020.11.05.368753

mifta-BRAF^V600E; p53 -/-; mifta -/-, irradiated and got ZMEL cell subcutaneous transplant to get tumors

whole fish with tumor

Code repo is not posted

Clustering; localization of GO terms in tissue; identifying GO terms with spatially coherent patterns with permutation testing; NMF on interface and muscle clusters; GO enrichment of top genes contributing to each NMF factor; marker genes for interface spots

R; Python; MATLAB

Memorial Sloan Kettering Cancer Center

Sloan Kettering

Cancer Biology and Genetics

Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics

https://doi.org/10.1101/2020.11.15.378125

Mus musculus

telencephalon and diencephalon

Visium data is here: https://cell2location.cog.sanger.ac.uk/browser.html

https://github.com/vitkl/cell2location_paper

Wellcome Sanger Institute

Upper cortical layer–driven network impairment in schizophrenia

Science Advances

https://doi.org/10.1126/sciadv.abn8367

Homo sapiens

dorsolateral prefrontal cortex (DLPFC) from Brodmann area 9 (BA9) (control)

Stereoscope for spot cell type deconvolution

University of Copenhagen

Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences

Upper cortical layer–driven network impairment in schizophrenia

Science Advances

https://doi.org/10.1126/sciadv.abn8367

Homo sapiens

dorsolateral prefrontal cortex (DLPFC) from Brodmann area 9 (BA9) (schizophrenia)

Repo is not yet available

University of Copenhagen

Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Communications Biology

https://doi.org/10.1038/s42003-021-02810-x

Mus musculus

Tibialis anterior muscles

2 days after notexin

cell type deconvolution

https://github.com/mckellardw/scMuscle

Cornell University

Meinig School of Biomedical Engineering

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Communications Biology

https://doi.org/10.1038/s42003-021-02810-x

Mus musculus

Tibialis anterior muscles

5 days after notexin

https://github.com/mckellardw/scMuscle

Cornell University

Meinig School of Biomedical Engineering

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Communications Biology

https://doi.org/10.1038/s42003-021-02810-x

Mus musculus

Tibialis anterior muscles

7 days after notexin

https://github.com/mckellardw/scMuscle

Cornell University

Meinig School of Biomedical Engineering

Single Cell and Spatial Transcriptomics Defines the Cellular Architecture of the Antimicrobial Response Network in Human Leprosy Granulomas

https://doi.org/10.1101/2020.12.01.406819

Homo sapiens

leprosy granulomas

Division of Dermatology, Department of Medicine

Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro

Nature Genetics

https://doi.org/10.1038/s41588-021-00972-2

Homo sapiens

postmortem full-thickness uterine tissue

https://github.com/ventolab/UHCA

Wellcome Sanger Institute

Spatiotemporal analysis of human intestinal development at single-cell resolution

https://doi.org/10.1016/j.cell.2020.12.016

Homo sapiens

University of Oxford

Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital

Spatiotemporal analysis of human intestinal development at single-cell resolution

https://doi.org/10.1016/j.cell.2020.12.016

Homo sapiens

University of Oxford

Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital

Spatiotemporal analysis of human intestinal development at single-cell resolution

https://doi.org/10.1016/j.cell.2020.12.016

Homo sapiens

University of Oxford

Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital

Three-dimensional spatial transcriptomics uncovers cell type dynamics in the rheumatoid arthritis synovium

Communications Biology

https://doi.org/10.1038/s42003-022-03050-3

Homo sapiens

Rheumatoid arthritis synovium knee biopsie

PRJNA794338

https://github.com/klarman-cell-observatory/3dst

R; Python; MATLAB

Broad Institute of MIT and Harvard

Klarman Cell Observatory

Three-dimensional spatial transcriptomics uncovers cell type dynamics in the rheumatoid arthritis synovium

Communications Biology

https://doi.org/10.1038/s42003-022-03050-3

Homo sapiens

Rheumatoid arthritis synovium hip biopsie

PRJNA794338

https://github.com/klarman-cell-observatory/3dst

R; Python; MATLAB

Broad Institute of MIT and Harvard

Klarman Cell Observatory

Spatial multi-omic map of human myocardial infarction

https://doi.org/10.1038/s41586-022-05060-x

Homo sapiens

myocardial infarction

2-5 days after the onset of clinical symptoms

They used MISTy. I only put the programming language used for Visium data analysis as I usually do.

https://github.com/saezlab/visium_heart

RWTH Aachen University

Institute of Experimental Medicine and Systems Biology

Spatial multi-omic map of human myocardial infarction

https://doi.org/10.1038/s41586-022-05060-x

Homo sapiens

myocardial infarction fibrotic zone

3 months after myocardial infarction

https://github.com/saezlab/visium_heart

RWTH Aachen University

Institute of Experimental Medicine and Systems Biology

Spatial multi-omic map of human myocardial infarction

https://doi.org/10.1038/s41586-022-05060-x

Homo sapiens

myocardial infarction fibrotic zone

12 years after myocardial infarction

https://github.com/saezlab/visium_heart

RWTH Aachen University

Institute of Experimental Medicine and Systems Biology

Spatial multi-omic map of human myocardial infarction

https://doi.org/10.1038/s41586-022-05060-x

Homo sapiens

https://github.com/saezlab/visium_heart

RWTH Aachen University

Institute of Experimental Medicine and Systems Biology