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Mattsson Langseth, C.

Publications and source records attributed to Mattsson Langseth, C..

6 recordsLinked to original sources

Open-source, high-throughput targeted in-situ transcriptomics for developmental biologists

Multiplexed spatial profiling of mRNAs has recently gained traction as a tool to explore the cellular diversity and the architecture of tissues. We propose a sensitive, open-source, simple and flexible method for the generation of in-situ expression maps of hundreds of genes. We exploit direct ligation of padlock probes on mRNAs, coupled with rolling circle amplification and hybridization-based in situ combinatorial barcoding, to achieve high detection efficiency, high throughput and large multiplexing. We validate the method across a number of species, and show its use in combination with orthogonal methods such as antibody staining, highlighting its potential value for developmental biology studies. Finally, we provide an end-to-end computational workflow that covers the steps of probe design, image processing, data extraction, cell segmentation, clustering and annotation of cell types. By enabling easier access to highthroughput spatially resolved transcriptomics, we hope to encourage a diversity of applications and the exploration of a wide range of biological questions.

molecular biology↗

Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10X Genomics capable of mapping hundreds of transcripts in situ at a subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore eight preview Xenium datasets of the mouse brain and two of human breast cancer by comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies. In addition, we benchmarked the performance of multiple open source computational tools when applied to Xenium datasets in tasks including cell segmentation, segmentation-free analysis, selection of spatially variable genes and domain identification, among others. This study serves as the first independent analysis of the performance of Xenium, and provides best-practices and recommendations for analysis of such datasets.

bioinformatics↗

Reference-based cell type matching of spatial transcriptomics data

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly. Spatial transcriptomics provides spatial location and pattern information about cells in tissue sections at single cell resolution. Cell type classification of spatially-resolved cells can also be inferred by matching the spatial transcriptomics data to reference single cell RNA-sequencing (scRNA-seq) data with cell types determined by their gene expression profiles. However, robust cell type matching of the spatial cells is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that while matching results of individual algorithms vary to some degree, they also show agreement to some extent. We present two ensembl meta-analysis strategies to combine the individual matching results and share the consensus matching results in the Cytosplore Viewer (https://viewer.cytosplore.org) for interactive visualization and data exploration. The consensus matching can also guide spot-based spatial data analysis using SSAM, allowing segmentation-free cell type assignment.

bioinformatics↗

Developmental landscape of human forebrain at a single-cell level unveils early waves of oligodendrogenesis

Oligodendrogenesis in the human central nervous system has been mainly observed at the second trimester of gestation, a much later developmental stage compared to mouse. Here we characterize the transcriptomic neural diversity in the human forebrain at post conceptual weeks (PCW) 8 to 10, using single-cell RNA-Seq. We find evidence of the emergence of a first wave of oligodendrocyte lineage cells as early as PCW 8, which we also confirm at the epigenomic level with single-cell ATAC-Seq. Using regulatory network inference, we predict key transcriptional events leading to the specification of oligodendrocyte precursor cells (OPCs). Moreover, by profiling the spatial expression of fifty key genes using In Situ Sequencing (ISS), we identify regions in the human ventral fetal forebrain where oligodendrogenesis first occurs. Our results indicate evolutionary conservation of the first wave of oligodendrogenesis between mouse and human and describe regulatory mechanisms required for human OPC specification.

neuroscience↗

Comprehensive in situ mapping of human cortical transcriptomic cell types

The ability to spatially resolve the cellular architecture of human cortical cell types over informative areas is essential to understanding brain function. We combined in situ sequencing gene expression data and single-nucleus RNA-sequencing cell type definitions to spatially map cells in sections of the human cortex via probabilistic cell typing. We mapped and classified a total of 59,816 cells into all 75 previously defined subtypes to create a first spatial atlas of human cortical cells in their native position, their abundances and genetic signatures.

neuroscience↗

Hybridization-based In Situ Sequencing (HybISS): spatial transcriptomic detection in human and mouse brain tissue

Visualization of the transcriptome in situ has proven to be a valuable tool in exploring single-cell RNA-sequencing data, providing an additional dimension to investigate spatial cell typing and cell atlases, disease architecture or even data driven discoveries. The field of spatially resolved transcriptomic technologies is emerging as a vital tool to profile gene-expression, continuously pushing current methods to accommodate larger gene panels and larger areas without compromising throughput efficiency. Here, we describe a new version of the in situ sequencing (ISS) method based on padlock probes and rolling circle amplification. Modifications in probe design allows for a new barcoding system via sequence-by-hybridization chemistry for improved spatial detection of RNA transcripts. Due to the amplification of probes, amplicons can be visualized with standard epifluorescence microscopes with high-throughput efficiency and the new sequencing chemistry removes limitations bound by sequence-by-ligation chemistry of ISS. Here we present hybridization-based in situ sequencing (HybISS) that allows for increased flexibility and multiplexing, increased signal-to-noise, all without compromising throughput efficiency of imaging large fields of view. Moreover, the current protocol is demonstrated to work on human brain tissue samples, a source that has proven to be difficult to work with image-based spatial analysis techniques. Overall, HybISS technology works as a target amplification detection method for improved spatial transcriptomic visualization, and importantly, with an ease of implementation.

molecular biology↗