ST Pipeline: an automated pipeline for spatial mapping of unique transcripts

Navarro, José Fernández; Sjöstrand, Joel; Salmén, Fredrik; Lundeberg, Joakim; Ståhl, Patrik L.

doi:10.1093/bioinformatics/btx211

Cited by 92 publications

(77 citation statements)

References 12 publications

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“…Final libraries were sequenced on the Illumina Next-Seq 500 and raw fastq files were processed through the ST pipeline [40], which involves removal of duplicate reads, homopolymer stretches, and reads with low quality. All plots were generated in R (version 3.5.1).…”

Section: Evaluation Of Libraries From Human Reference Rnamentioning

confidence: 99%

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

et al. 2020

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Background: Interest in studying the spatial distribution of gene expression in tissues is rapidly increasing. Spatial Transcriptomics is a novel sequencing-based technology that generates high-throughput information on the distribution, heterogeneity and co-expression of cells in tissues. Unfortunately, manual preparation of high-quality sequencing libraries is time-consuming and subject to technical variability due to human error during manual pipetting, which results in sample swapping and the accidental introduction of batch effects. All these factors complicate the production and interpretation of biological datasets. Results: We have integrated an Agilent Bravo Automated Liquid Handling Platform into the Spatial Transcriptomics workflow. Compared to the previously reported Magnatrix 8000+ automated protocol, this approach increases the number of samples processed per run, reduces sample preparation time by 35%, and minimizes batch effects between samples. The new approach is also shown to be highly accurate and almost completely free from technical variability between prepared samples. Conclusions: The new automated Spatial Transcriptomics protocol using the Agilent Bravo Automated Liquid Handling Platform rapidly generates high-quality Spatial Transcriptomics libraries. Given the wide use of the Agilent Bravo Automated Liquid Handling Platform in research laboratories and facilities, this will allow many researchers to quickly create robust Spatial Transcriptomics libraries.

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Section: Evaluation Of Libraries From Human Reference Rnamentioning

confidence: 99%

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

et al. 2020

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“…Tissue sections were then digested using proteinase K (Qiagen) and the barcode information cleaved using a Uracil-Specific Excision Reagent (NEB) targeting the 5d(U) stretch at the 5' end on the barcoded oligonucleotides. The collected material was then processed into libraries as described in Jemt et al 18 HDST data pre-processing FASTQ files were processed using the ST Pipeline v1.5.0 software 19 . The forward read contained both the barcode sequence and the bridge sequence used for the sequential ligation steps.…”

Section: Rna-seq Library Preparation and Sequencingmentioning

confidence: 99%

“…Mapped reads were counted using the HTseq count tool 24 . Spatial barcodes were demultiplexed using an optimized implementation of TagGD 25 as described 22 . We allowed a 2 bp "padding" overhang on the total length of the spatial barcode.…”

Section: Rna-seq Library Preparation and Sequencingmentioning

confidence: 99%

High-density spatial transcriptomics arrays for in situ tissue profiling

Vicković

Eraslan

Salmén

et al. 2019

Preprint

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Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays either lack global transcriptome information or are not at the single-cell level. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are then randomly deposited in individual wells on a slide. The barcoded beads are decoded and coupled to a specific spatial address. We then capture and spatially in situ label RNA from the same histological tissue sections placed on the bead array slide. HDST recovers hundreds of thousands of transcript-coupled barcodes per experiment at 2 µm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to 2D spatial analysis of tissues at high resolution. Author information AffiliationsBroad Institute of MIT and Harvard,

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“…3g and S6e). From outside inwards, these clusters correspond to the olfactory nerve layer, the glomerular layer, 4 draft-534-g100f761 the plexiform and mitral cell layers, as well as two for the granular cell layer: a peripheral and a central one. The outer plexiform, mitral, and inner plexiform layers cannot clearly be resolved by partitioning into more clusters (results not shown), presumably due to insufficient spatial resolution of the array spots.…”

Section: Mouse Olfactory Bulbmentioning

confidence: 99%

“…In the classical setting, annotated covariates dictate how samples are to be grouped and compared. These covariates are typically known, and often controlled for, as is the case when performing differential gene expression (DGE) analysis for bulk sequencing count data in DESeq2 [4]. In contrast, the covariates determining gene expression in space are often unknown and can change both gradually or abruptly.…”

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confidence: 99%

Charting Tissue Expression Anatomy by Spatial Transcriptome Decomposition

Maaskola

Bergenstråhle

Jurek

et al. 2018

Preprint

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We create data-driven maps of transcriptomic anatomy with a probabilistic framework for unsupervised pattern discovery in spatial gene expression data. Convolved negative binomial regression is used to find patterns which correspond to cell types, microenvironments, or tissue components, and that consist of gene expression profiles and spatial activity maps. Expression profiles quantify how strongly each gene is expressed in a given pattern, and spatial activity maps reflect where in space each pattern is active. Arbitrary covariates and prior hierarchies can be specified to support and leverage complex experimental designs.Transcriptomic patterns in mouse brain and olfactory bulb correspond to neuroanatomically distinct cell layers. Moreover, batch effects are successfully addressed, leading to consistent pattern inference for multi-sample analyses. On this basis, we identify known and uncharacterized genes that are spatially differentially expressed in the hippocampal field between Ammon's horn and the dentate gyrus.The analysis of spatially stratified, transcriptome-wide data [1][2][3] poses additional challenges compared to classical analysis of bulk RNA sequencing (RNA-Seq) samples. In the classical setting, annotated covariates dictate how samples are to be grouped and compared. These covariates are typically known, and often controlled for, as is the case when performing differential gene expression (DGE) analysis for bulk sequencing count data in DESeq2 [4]. In contrast, the covariates determining gene expression in space are often unknown and can change both gradually or abruptly. For example, the number of infiltrating immune cells per unit area often If the cell types underlying a sample are well characterized, it becomes analytically possible to determine mixing proportions of the cell types' known expression profiles in spatial gene expression data. But in the general case, there exists the dual discovery problems of expression profiles and spatial distributions.Two recently introduced statistical tests, Trendsceek and SpatialDE, quantify the extent of spatial variation of individual genes' expression [5,6]. Transcending individual genes, the "automatic expression histology" feature of SpatialDE implements a gene clustering approach to hidden pattern discovery. Such clustering formulations, however, appear challenged by genes that participate in multiple, spatially overlapping expression programs, and grade-of-membership formulations seem more appropriate for hidden pattern discovery.Joint analysis of multiple data sets substantially increases statistical power and sensitivity but is challenging due to the presence of batch effects. Both nonparametric [7,8] and model-based approaches are used to address such effects in the analysis of single cell RNA sequencing (scRNA-Seq) data. The model-based methods perform regression for the count data based on known sample-level covariates and allow for the discovery of unknown ones.Log-normal expression models are used for bulk [9,10] and single cell [11,12] R...

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ST Pipeline: an automated pipeline for spatial mapping of unique transcripts

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 92 publications

References 12 publications

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

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

High-density spatial transcriptomics arrays for in situ tissue profiling

Charting Tissue Expression Anatomy by Spatial Transcriptome Decomposition

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