Biology subjects

Treppner, M.

Publications and source records attributed to Treppner, M..

6 recordsLinked to original sources

bioRxiv2024

Enhancement of network architecture alignment in comparative single-cell studies

1Animal data can provide meaningful context for human gene expression at the single-cell level. This context can improve cell-type or cell-state detection and clarify how well the animal models human biological processes. To achieve this, we propose a deep learning approach that identifies a unified latent space to map complex patterns between datasets. Specifically, we combine variational autoencoders with a data-level nearest neighbor search to align neural network architectures across species. We visualize commonalities by mapping cell samples into the latent space. The aligned latent representation facilitates information transfer in applications of liver, white adipose tissue, and glioblastoma cells from various animal models. We also identify genes that exhibit systematic differences and commonalities between species. The results are robust for small datasets and with large differences in the observed gene sets. Thus, we reliably uncover and exploit similarities between species to provide context for human single-cell data.

bioinformatics↗

bioRxiv2022

Designing Single-Cell RNA-Sequencing Experiments for Learning Latent Representations

To investigate the complexity arising from single-cell RNA-sequencing (scRNA-seq) data, researchers increasingly resort to deep generative models, specifically variational autoencoders (VAEs), which are trained by variational inference techniques. Similar to other dimension reduction approaches, this allows encoding the inherent biological signals of gene expression data, such as pathways or gene programs, into lower-dimensional latent representations. However, the number of cells necessary to adequately uncover such latent representations is often unknown. Therefore, we propose a single-cell variational inference approach for designing experiments (scVIDE) to determine statistical power for detecting cell group structure in a lower-dimensional representation. The approach is based on a test statistic that quantifies the contribution of every single cell to the latent representation. Using a smaller scRNA-seq data set as a starting point, we generate synthetic data sets of various sizes from a fitted VAE. Employing a permutation technique for obtaining a null distribution of the test statistic, we subsequently determine the statistical power for various numbers of cells, thus guiding experimental design. We illustrate with several data sets from various sequencing protocols how researchers can use scVIDE to determine the statistical power for cell group detection within their own scRNA-seq studies. We also consider the setting of transcriptomics studies with large numbers of cells, where scVIDE can be used to determine the statistical power for sub-clustering. For this purpose, we use data from the human KPMP Kidney Cell Atlas and evaluate the power for sub-clustering of the epithelial cells contained therein. To make our approach readily accessible, we provide a comprehensive Jupyter notebook at https://github.com/MTreppner/scVIDE.jl that researchers can use to design their own experiments based on scVIDE.

bioinformatics↗

bioRxiv2022

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the existing literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider eight popular tools in more detail and examine their robustness to different cell numbers, covering two of the most common multi-omics types currently favored. Specifically, we use data featuring simultaneous gene expression measurements at the RNA level and protein abundance measurements for cell surface proteins (CITE-seq), as well as data where chromatin accessibility and RNA expression are measured in thousands of cells (10x Multiome). We examine the ability of the methods to learn joint embeddings based on biological and technical metrics. Finally, we provide recommendations for the design of multi-omics experiments and discuss potential future developments.

bioinformatics↗

bioRxiv2022

DOT1L activity affects cell lineage progression in the developing brain by controlling metabolic programs

Cortical neurogenesis depends on the tight balance between self-renewal and differentiation of apical progenitors (APs), the key progenitor type generating all other neural cells including neocortical neurons. We here report the activity of the histone methyltransferase DOT1L as a gatekeeper for AP cell identity. Combining lineage tracing with single-cell RNA sequencing of clonally related cells, we explore consequences of DOT1L inhibition on AP lineage progression during neurogenesis in the embryonic mouse neocortex. At the cellular level, DOT1L inhibition led to increased neurogenesis driven by a shift from asymmetric self-renewing to symmetric neurogenic divisions of APs. At the molecular level, we show that DOT1L activity preserved AP identity by promoting transcription of a gene set involved in AP metabolism. On a mechanistic level, DOT1L inhibition increased expression of metabolic genes, including microcephaly-associated Asparagine synthetase (Asns) and overexpression of ASNS in APs resulted in increased neuronal differentiation. Asns expression was predicted to be controlled through EZH2 and we show that DOT1L activity allows PRC2-mediated repression of Asns expression. Importantly, inhibition of ASNS activity rescued increased AP differentiation upon DOT1L inhibition. Our data show that DOT1L activity/PRC2 crosstalk controls AP lineage progression by regulating AP metabolism, and they provide a mechanistic view on how DOT1L activity might affect neocortical neurogenesis.

developmental biology↗

bioRxiv2021

Single-cell transcriptomic resolution of stem cells and their developmental trajectories in the hippocampus reveals epigenetic control of cell state perseverance

Despite conceptual research on hippocampus development and the application of single-cell-resolved technologies, the nature and maturation of its diverse progenitor populations are unexplored. The chromatin modifier DOT1L balances progenitor proliferation and differentiation, and conditional loss-of-function mice featured impaired hippocampus development. We applied single-cell RNA sequencing on DOT1L-mutant mice and explored cell trajectories in the E16.5 hippocampus. We resolved in our data five distinct neural stem cell populations with the developmental repertoire to specifically generate the cornu ammonis (CA) 1 field and the dentate gyrus (DG). Within the two developing CA1- and CA3-fields, we identified two distinct maturation states and we thus propose CA1- and CA3-differentiation along the radial axis. In the developing hippocampus, DOT1L is primarily involved in the proper development of CA3 and the DG, and it serves as a state-preserving epigenetic factor that orchestrates the expression of several important transcription factors that impact neuronal differentiation and maturation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/452775v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@d611caorg.highwire.dtl.DTLVardef@2a0e3corg.highwire.dtl.DTLVardef@10c9082org.highwire.dtl.DTLVardef@1e358c8_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIThe developing hippocampus contains distinct and spatially separated NSC populations that differ in expression of a specific set of firstly described marker genes. C_LIO_LICA pyramidal neurons mature along the radial axis and pass through distinct maturation states. C_LIO_LIDOT1L preserves the dentate granule cell lineage in the developing hippocampus and limits maturation in the CA1- and CA3-fields development. C_LIO_LIDOT1L gates cell maturation as upstream regulator of transcription factor expression that confer instrumental roles in hippocampus development. C_LI

neuroscience↗

bioRxiv2020

Generating Synthetic Single-Cell RNA-Sequencing Datafrom Small Pilot Studies using Deep Learning

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBM), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale study by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBM). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the scVIposterior variant resulted in high variability, most likely due to amplifying artifacts of small data sets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Overall, the results showed that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

bioinformatics↗