Biology subjects

Tiesmeyer, S.

Publications and source records attributed to Tiesmeyer, S..

6 recordsLinked to original sources

bioRxiv2025

2D, or not 2D? Investigating Vertical Signal Integrity of Tissue Slices

Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in 3D. Cell segmentation assigns transcripts to cells and precedes annotation of cell function. However, cell segmentation is usually performed in 2D, thus unable to deal with spatial doublets arising from overlapping cells, resulting in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds, and inaccurate cell-segmentation by analyzing transcript localization in 3D.

bioinformatics↗

bioRxiv2024

Sainsc: a computational tool for segmentation-free analysis of in-situ capture

Spatially resolved transcriptomics has become the method of choice to characterise the complexity of biomedical tissue samples. Until recently, scientists have been restricted to profiling methods with high spatial resolution but for a limited set of genes or methods that can profile transcriptome-wide but at low spatial resolution. Through recent developments, there are now methods which offer subcellular spatial resolution and full transcriptome coverage. However, utilizing the high spatial and gene resolution of these new methods remains elusive due to several factors including low detection efficiency, high computational cost and difficulties in delineating cell borders. Here we present Sainsc (Segmentation-free analysis of in-situ capture data), which combines a cell-segmentation free approach with efficient data processing of transcriptome-wide nanometer resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at a subcellular level, together with corresponding maps of the assignment scores that facilitate the interpretation in the local confidence of cell-type assignment. We demonstrate its utility and accuracy across different tissues and profiling methods. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

bioinformatics↗

bioRxiv2023

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↗

bioRxiv2022

Disturbed trophoblast transition links preeclampsia progression from placenta to the maternal syndrome

Pre-eclampsia (PE) is a syndrome that affects multiple organ systems and is the most severe hypertensive disorder in pregnancy. It frequently leads to preterm delivery, maternal and fetal morbidity and mortality and life-long complications1. We currently lack efficient screening tools2, 3 and early therapies4, 5 to address PE. To investigate the early stages of early onset PE, and identify candidate markers and pathways, we performed spatio-temporal multi-omics profiling of human PE placentae and healthy controls and validated targets in early gestation in a longitudinal clinical cohort. We used a single-nuclei RNA-seq approach combined with spatial proteo- and transcriptomics and mechanistic in vitro signalling analyses to bridge the gap from late pregnancy disease to early pregnancy pathomechanisms. We discovered a key disruption in villous trophoblast differentiation, which is driven by the increase of transcriptional coactivator p300, that ultimately ends with a senescence-associated secretory phenotype (SASP) of trophoblasts. We found a significant increase in the senescence marker activin A in preeclamptic maternal serum in early gestation, before the development of clinical symptoms, indicating a translation of the placental syndrome to the maternal side. Our work describes a new disease progression, starting with a disturbed transition in villous trophoblast differentiation. Our study identifies potential pathophysiology-relevant biomarkers for the early diagnosis of the disease as well as possible targets for interventions, which would be crucial steps toward protecting the mother and child from gestational mortality and morbidity and an increased risk of cardiovascular disease later in life.

systems biology↗

bioRxiv2021

SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

1The combination of a cells transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data. Availability and ImplementationSSAM-lite is an open-source browser-based web application with source code freely available on Github via https://github.com/HiDiHlabs/ssam-lite. Stable releases can be accessed via https://ssam-lite.bihealth.org and https://ssam-lite.netlify.app, and developmental releases can be accessed via https://dev--ssam-lite.netlify.app. The source code for a locally deployable server version, SSAM-lite-server, is available on GitHub via https://github.com/HiDiHlabs/ssam-lite-server. Both versions require a modern browser with JavaScript and WebGL support. Detailed user guides and documentation can be found at https://ssam-lite.readthedocs.io.

bioinformatics↗

bioRxiv2019

Segmentation-free inference of cell-types from in situ transcriptomics data

Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a novel method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. We found that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

bioinformatics↗