Biology subjects

Schuierer, S.

Publications and source records attributed to Schuierer, S..

6 recordsLinked to original sources

bioRxiv2022

Transcriptional profiling of hepatocytes infected with the replicative form of the malaria parasite Plasmodium cynomolgi

The zoonotic simian parasite Plasmodium cynomolgi develops into replicating schizonts and dormant hypnozoites during the infection of hepatocytes and is used as a model organism to study relapsing malaria. We previously reported the transcriptional profiling of P. cynomolgi liver stages and revealed many important biological features of the parasite (Bertschi et al., Elife, 2018; Voorberg-van der Wel et al., Elife, 2017) but left out the host response to malaria infection. Here, we used our published RNA sequencing data to quantify the expression of host genes in rhesus macaque hepatocytes infected with P. cynomolgi in comparison to either cells from uninfected samples or uninfected bystander cells. Although the dataset could not be used to resolve the transcriptional profile of hypnozoite-infected hepatocytes, it provided a snapshot of the host response to liver stage schizonts and identified specific host pathways that are modulated during malaria infection. This study constitutes a valuable resource characterizing the hepatocyte response to P. cynomolgi infection and provides a framework to build on future research that aims at understanding hepatocyte-parasite interactions during relapsing malaria infection.

microbiology↗

bioRxiv2022

Probabilistic modeling of ambient noise in single-cell omics data

Droplet-based single-cell omics, including single-cell RNA sequencing (scRNAseq), single-cell CRISPR perturbations (e.g., CROP-seq), and single-cell protein and transcriptomic profiling (CITE-seq) hold great promise for comprehensive cell profiling and genetic screening at the single-cell resolution. However, these technologies suffer from substantial noise, among which ambient signals present in the cell suspension may be the predominant source. Current models to address this issue are highly technology-specific and relatively scRNAseq-centric. while a universal model to describe the noise across these technologies may reveal this common source, improving the denoising accuracy. To this end, we explicitly examined these unexpected signals in multiple datasets across droplet-based technologies, summarised a predictable pattern, and developed single-cell Ambient Remover (scAR) - a hypothesis-driven machine learning model to predict and remove ambient signals (including mRNA counts, protein counts, and sgRNA counts) at the molecular level. We benchmarked scAR on three technologies - single-cell CRISPR screens, CITE-seq, and scRNAseq along with the state-of-the-art single-technology-specific approaches. scAR showed high denoising accuracy for each type of dataset.

bioinformatics↗

bioRxiv2021

Persistence of Plasmodium cynomolgi hypnozoites in cynomolgus monkey iPS-derived hepatocytes

Plasmodium cynomolgi (Pc) is one of the few parasite species that forms quiescent liver stage parasites known as hypnozoites and is therefore a suitable model for Plasmodium vivax. Very little is known about liver stage dormancy, which hampers the search for compounds with anti-hypnozoite activity. Here, we present the development of a Pc in vitro infection model using stem cell-derived hepatocytes from Macaca fascicularis. IPS cells were established on feeder free condition and differentiated into hepatocytes via inducible overexpression of key transcription factors. The generated hepatocytes were infected with Pc sporozoites and hypnozoite formation as well as schizont development were confirmed by immunofluorescence. This system is a promising tool to study the mechanisms underlying liver stage dormancy and facilitate drug discovery against hypnozoites.

cell biology↗

bioRxiv2019

Cell types of the human retina and its organoids at single-cell resolution: developmental convergence, transcriptomic identity, and disease map

How closely human organoids recapitulate cell-type diversity and cell-type maturation of their target organs is not well understood. We developed human retinal organoids with multiple nuclear and synaptic layers. We sequenced the RNA of 158,844 single cells from these organoids at six developmental time points and from the periphery, fovea, pigment epithelium and choroid of light-responsive adult human retinas, and performed histochemistry. Cell types in organoids matured in vitro to a stable developed state at a rate similar to human retina development in vivo and the transcriptomes of organoid cell types converged towards the transcriptomes of adult peripheral retinal cell types. The expression of disease-associated genes was significantly cell-type specific in adult retina and cell-type specificity was retained in organoids. We implicate unexpected cell types in diseases such as macular degeneration. This resource identifies cellular targets for studying disease mechanisms in organoids and for targeted repair in adult human retinas.

neuroscience↗

bioRxiv2019

A simple and efficient CRISPR technique for protein tagging

Genetic knock-in using homology directed repair is an inefficient process, requiring selection of few modified cells and hindering its application to primary cells. Here we describe Homology independent gene Tagging (HiTag), a method to tag a protein of interest by CRISPR in up to 66% transfected cells with one single electroporation. The technique has proven effective in various cell types, can be used to knock in a fluorescent protein for live cell imaging, to modify the cellular location of a target protein and to monitor levels of a protein of interest by a luciferase assay in primary cells.

cell biology↗

bioRxiv2019

CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data

Comprehensive benchmarking of computational methods for single-cell RNA sequencing (scRNA-seq) analysis is scarce. Using a modular workflow and a large dataset with known cell composition, we benchmarked feature selection and clustering methodologies for scRNA-seq data. Results highlighted a methodology gap for rare cell population identification for which we developed CellSIUS (Cell Subtype Identification from Upregulated gene Sets). CellSIUS outperformed existing approaches, enabled the identification of rare cell populations and, in contrast to other methods, simultaneously revealed transcriptomic signatures indicative of the rare cells function. We exemplified the use of our workflow and CellSIUS for the characterization of a human pluripotent cell 3D spheroid differentiation protocol recapitulating deep-layer corticogenesis in vitro. Results revealed lineage bifurcation between Cajal-Retzius cells and layer V/VI neurons as well as rare cell populations that differ by migratory, metabolic, or cell cycle status, including a choroid plexus neuroepithelial subgroup, revealing previously unrecognized complexity in human stem cell-derived cellular populations.

bioinformatics↗