#spatialTranscriptomics

Want to visualize & create a website for your omics data (+CITEseq scATACseq #SpatialTranscriptomics)? Try ShinyCell2!😆 Compatible with data formats Seurat Signac ArchR Scanpy (other formats converted by sceasy) (also imaging-based ST data?😁) vs cellxgene WebAtlas Vitessce @OwenRackham @imouyang bioRxiv 2024

Imaging-based #SpatialTranscriptomics #Segmentation Segger 👉Attention-based #GraphNeuralNetwork 👉Learn a transcript-cell/boundary joint latent space 👉Unassigned transcripts➡️fragments➡️ median 20 transcripts/fragment vs 50 transcripts/cell vs Baysor BIDCell CellPose 10X Cell/Nuclei protocol 30 times faster than Baysor (on 4x @NVIDIAAI A100 GPU...🤧💸💸💸🤑) @HeidariElyas @MoritzGerstung bioRxiv 2025

We are excited to share our new preprint about our development of a new #spatialTranscriptomics method (sphere-sequencing) to unveil local tissue microenvironments at single cell resolution! See below the #tweetorial from our very own @handlerkristina 👩‍🔬

A guidebook of spatial transcriptomic technologies, data resources & analysis approaches🤠 A great ending for my 2-wk learning of #SpatialTranscriptomics tools Start reading real-world #SpatialOmics & preparing for @IVBM2024😆 @CSB_Journal 👉Jan 2023

DeepSpaceDB >2k #SpatialTranscriptomics Visium samples 👉Interactive, downloadable data analysis QC➡️ manual/LLM image annotation➡️ in-sample or cross-sample spot clustering BASS BayesSpace➡️ Spatially variable genes singleCellHaystack SPARK-X binSpect➡️ Cell type deconvolution RCTD SPOTlight 👉Cross-sample/Cross-study comparison 👉Analyze your own data 👉Database-wide gene/pathway inquiry @alexisvdb #NucAcidRes 2026

#SpatialRNAEncyclopedia Patho-DBiT #SpatialTranscriptomics FFPE Spatial Co-mapping of RNA Expression Splicing editing SNV AND ncRNAs (miRNA lncRNA rRNA snRNA snoRNA & more)👹 in situ polyA tailing all RNAs 10 μm/px (near single cell)➡️ >2000 genes > 6000 UMIs/px Lymphoma @Zhiliang_Bai @DrMingyaoLi @YiXing77 @JunLuLab @MinaXu7 @RongFan8 @CellCellPress 2025

I used gganimate in #Rstats to tell a story about #singlecell #spatialtranscriptomics #bioinformatics #dataanalysis Code: If a picture is worth a 1000 words, perhaps an animation is worth a million? What's your interpretation of this #dataviz story?

Curious about what we can do? #SpatialTranscriptomics - all mRNAs spatially resolved. See our showcase:

Systematic comparisons among 4 subcellular-resolution #SpatialTranscriptomics methods Stereo-seq v1.3 Visium HD FFPE CosMx 6k Xenium 5k Human Tumors Ground truth CODEX, +scRNAseq Gene detection sensitivity (CosMx performance...🙁) (Spatial) false positives Transcript-protein correlation Cell segmentation Cell type annotation & spatial alignment to CODEX Spatial clustering Spatial pathway enrichment @NatureComms 2025

#SpatialTranscriptomics #ThoracicAorticDisease Human aorta is a mm-thick layered organ (50~60 circumferential Elastic-contractile units in adult) Its lumen & adventitia sides receive different chemical/mechanical inputs Any phenotypic/signaling gradients of #SmoothMuscleCells across the aortic medial depth?😆👇 Here is a very cool & informative study from one of my most favorite scientists, Dr. Hiromi Yanagisawa @HYanagisawaLab 👏 Micro-biopsies RNAseq from 4 distinct layers in human aortic media 5 #BicuspidAorticValue + 4 Sporadic Extracellular Matrix signaling (FN1/VCAN/MGP/POSTN) enriched in innermost layer (Beautiful POSTN IHC😍) TGFβ BAV > Sporadic Inflamm Sporadic > BAV Mechanosensing in innermost layer Sporadic > BAV🥸 #AortaEd #JVSVS 2026 @JVascSurg

I am updating my lecture slides on #spatialtranscriptomics to use author names + remove journal names to de-emphasize brands and re-emphasize people per #HHMI recs. In the process, I realized ALL the seminal techdev work has been led by co-first authors contributing equally 🧵1/3

🚨New #SpatialTranscriptomics #Bioinformatics data resource out in @naturemethods. SODB, a platform with >2,400 manually curated spatial experiments from >25 spatial omics technologies & interactive analytical modules. This🧵will walk you through all the features of SODB [1/33]

#CellCharter A powerful single-cell #SpatialTranscriptomics analysis framework Graph-based #DeepLearning Spatial cluster: Cell type Different expression Neighborhood enrichment AND Shape!!!👹 - curl/elongation/linearity/purity #VariationalAutoEncoder for dimension reduction, batch correction & multi-technical platform (CosMx/MERFISH/#ImagingMassSpectrometry)/Multiomic integration Spatially-resolved #LungCancer Hypoxia & Tumore-associated neutrophil signatures->Prognostic values for NSCLC Computationally efficiency->Scalability @MarcoVarrone @loganawalsh @CirielloLab @NatureGenet 2024

stLearn Spatially aware inference of Trajectory & Cell-Cell Interactions➡️ Pseudo-Time-Space #SpatialTranscriptomics Spatial-PAGA stSME Graph-based imputation to correct technical dropouts #Visium @MarcRuitenberg @Quan_Nguyen_UQ @NatureComms 2023

STCellbin Use Nuclei+Cell Membrane/Wall stains to improve #CellSegmentation & single-cell-resolved #SpatialTranscriptomics Tailored for #StereoSeq, but likely implementable for other ST methods Susanne Brix & Xun Xu labs @GigaByteJournal 2024

TransformerST #SpatialTranscriptomics Vision transformer Adaptive graph transformer Cross-scale internal graph network Image-gene co-representation: Gene expression+Spatial coordinates+Histologic images "could achieve super-resolved resolution of a single cell per subspot" w/o scRNAseq reference Note for Lung Microvascular researchers with #VISIUM ✅stLearn SpaGCN TransformerST @BriefingBioinfo 2024 If you are a #SpatialOmics learner like me, this is a particularly useful, timely piece of reading 😀

We introduce #POPARI, an interpretable, spatially-aware factor-based model for multi-sample #spatialtranscriptomics. Huge kudos to @alam_shahul for his incredible effort (yes, 80+ equations!). In collaboration with @immunoliugy and @insitubiology.

#VisiumHD #SpatialTranscriptomics #ColorectalCancer FFPE sections Continuous 2-µm matrix 8-µm binned data "deconvolution based on single cell data and assignment of uniform labels is useful for performing comparisons between samples" 2-µm bins assigned to nuclei polygon▶️gene by nuclei UMI matrix "Reanalyzing the data in this region using higher resolution (2 µm bins), enabled us to improve our capacity to identify T cells within the TME, since many were lost due to their small size and colocalization with other cells at 8 µm resolution" @10xGenomics bioRxiv 2024

We really recommend you to read this article called: "Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity": #SpatialTranscriptomics

TRISCO A solid step closer to 3D #SpatialTranscriptomics 😎 Optimized probe-based 3D RNA imaging in whole organs isHCR Tris 500 mM 4'C Validated probe list in Table S1 Whole brain: mouse/rat/guinea pig Brain Hemisphere: human fetus Heart Lung Kidney: mouse @kanashigeaki @peruhlen @ScienceMagazine 2024