Biology subjects

Cuttat, R.

Publications and source records attributed to Cuttat, R..

6 recordsLinked to original sources

bioRxiv2025

Sample-level modeling of single-cell data at scale with tinydenseR

Single-cell studies now routinely encompass hundreds of samples and millions of cells, offering unprecedented opportunities to link sample-level phenotypes with cellular and molecular states. However, current workflows often depend on cell-level inference and rigid clustering, which can distort significance and obscure subtle, continuous variation, in particular for complex experimental designs. Here, we present tinydenseR, a clustering-independent framework that enables robust, scalable, and statistically sensitive detection of differential cell state density, outperforming existing workflows in speed and memory usage. Technology-agnostic at its core, tinydenseR works seamlessly on scRNA-seq, flow, mass and spectral cytometry. Across synthetic benchmarks, a preclinical xenograft model, a publicly available COVID-19 study, two immuno-oncology trials and a multi-study atlas, tinydenseR uncovers disease and treatment history-associated effects, including subtle cell state heterogeneity, while embedding samples in a quantitative manner. Designed to accelerate discovery in clinical, preclinical, and translational research, the open-source package is available at GitHub.com/Novartis/tinydenseR.

bioinformatics↗

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

Sustained TREM2 stabilization accelerates microglia heterogeneity and Abeta pathology in a mouse model of Alzheimer s disease

TREM2 is a transmembrane protein expressed exclusively in microglia in the brain that regulates inflammatory responses to pathological conditions. Proteolytic cleavage of membrane TREM2 affects microglial function and is associated with Alzheimers disease, but the consequence of reduced TREM2 proteolytic cleavage has not been determined. We generated a transgenic mouse model of reduced TREM2 shedding (Trem2-IPD) through amino acid substitution of ADAM-protease recognition site. We found that Trem2-IPD mice displayed increased TREM2 cell surface receptor load, survival and function in myeloid cells. Using single cell transcriptomic profiling of mouse cortex we show that sustained TREM2 stabilization induces a shift of fate in microglial maturation and accelerates microglial responses to A{beta} pathology in a mouse model of Alzheimers disease. Our data indicate that reduction of TREM2 proteolytic cleavage aggravates neuroinflammation during the course of AD pathology suggesting that TREM2 shedding is a critical regulator of microglial activity in pathological states.

neuroscience↗

bioRxiv2021

Genome-wide screening in human kidney organoids identifies novel aspects of nephrogenesis

Human organoids allow studying proliferation, lineage specification, and three-dimensional tissue development. Due to the inherent multicellular complexity, interrogation by systematic genetic methodologies is challenging. Here, we present the first genome-wide CRISPR screen in iPSC-derived kidney organoids. The combination of genome editing, longitudinal sampling and sorting of specific cell populations enabled us to uncover novel biology from development to tubular morphogenesis. We validated the high quality of our screen by individual hit follow up and comparisons to kidney disease datasets. The discovery of a novel regulatory mechanism which controls epithelial proliferation via a trans-activating but cis-inhibitory effect of the Notch ligand Jagged1 proves how mosaic knockouts generated by pooled CRISPR screening in organoids can identify novel ways of communication between heterogeneous cell populations in complex tissues. Collectively, these data demonstrate the feasibility of using complex iPSC- derived organoids for genome-scale screening and serves as a benchmark for future organoid CRISPR screens.

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

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↗