Biology subjects

Schleicher, J. T.

Publications and source records attributed to Schleicher, J. T..

3 recordsLinked to original sources

bioRxiv2026

G-LATO: Inference of Spatial Latent Ordering via Deep Gaussian Processes

Spatial transcriptomics enables the study of cells within their native tissue context, yet identifying gradients of cellular development remains challenging. We introduce a deep Gaussian process model to address this gap. Our method recovers spatially smooth gradients explaining observed gene expression. We illustrate our method on healthy liver and glioblastoma data in reconstructing known spatial organisation and uncovering new pathological gradients, thus providing robust inference for spatial biology.

bioinformatics↗

bioRxiv2025

Accurate quantification of spliced and unspliced transcripts for single-cell RNA sequencing with tidesurf

MotivationSingle-cell RNA sequencing (scRNA-seq) allows for the detailed analysis of dynamic cellular processes. In particular, this has been enabled by the estimation of RNA velocity, the derivative of gene expression, from separate count matrices for different splice states, which provides information about a cells immediate future even in snapshot data. Useful velocity estimates strongly depend on accurate counts for spliced and unspliced transcripts. Velocyto remains the standard tool for spliced and unspliced mRNA molecule quantification. However, despite considerable advances in scRNA-seq protocols, velocyto has not been updated to account for peculiarities of new protocols, such as popular approaches based on 5 chemistry. ResultsTo address this shortcoming, we present tidesurf, a command line tool for the quantification of spliced and unspliced transcript molecules from scRNA-seq libraries. Employing it on four different publicly available 10x Genomics Chromium datasets, we show the accuracy on various datasets generated with either 3 or 5 chemistry, whereas velocytos results are highly erroneous for the latter. Considering broader applicability, our results highlight tidesurf as a potential replacement for velocyto. Availability and implementationA Python implementation of tidesurf is available from PyPI and at github.com/janschleicher/tidesurf. Code for reproducing the analyses is available at github.com/janschleicher/tidesurf projects.

bioinformatics↗

bioRxiv2025

Quantification of spliced and unspliced transcripts by velocyto is inaccurate for 5'-sequencing data

RNA velocity allows for the prediction of future states of individual cells based on their current transcriptional activity. The technique depends on the separate quantification of spliced and unspliced transcripts in single-cell RNA-sequencing data, first introduced by velocyto in 2018. Since its introduction, significant advances have been made in the field, including new protocols by 10x Genomics that enable sequencing from the 5-end of mRNA molecules instead of the 3-end. Despite these advances, velocyto has not been updated since its release but is still commonly used with these new protocols. In this study, we demonstrate that velocyto cannot accurately detect the reversed direction of transcripts, leading to incorrect count assignments. By comparing velocyto to alevin-fry, a quantification method compatible with 5-sequencing data, we show that this limitation can result in substantial deviations in inferred velocities and differing interpretations. Therefore, we do not recommend the use of velocyto with 5-sequencing data.

bioinformatics↗