Biology subjects

Khven, I.

Publications and source records attributed to Khven, I..

7 recordsLinked to original sources

bioRxiv2025

SEQURNA enhances FLASH-seq gene detection while eliminating DTT dependence

Effective RNase inhibition is critical for single-cell RNA-sequencing, yet commercial recombinant RNase inhibitors (RRIs) require reducing agents for stability and impose substantial costs. Here, we systematically benchmark SEQURNA, a synthetic thermostable RNase inhibitor, against commercial alternatives using FLASH-seq in human retinal organoids and peripheral blood mononuclear cells (PBMCs). SEQURNA at 0.5-1 U/l achieved 14-50% higher gene detection than Takara RRI, with the greatest improvement in low-RNA PBMCs. Surprisingly, DTT supplementation at standard concentrations (5-10 mM) significantly impaired gene detection across all SEQURNA concentrations without improving RNA quality metrics, challenging established reverse transcription protocols. SEQURNA preserved biological heterogeneity, maintained sample stability during one-month storage at -80{degrees}C, and reduced reagent costs by 70%. We recommend SEQURNA at 1 U/l without DTT as an optimized formulation that simultaneously enhances data quality and cost-effectiveness for full-length single-cell RNA sequencing.

genomics↗

bioRxiv2025

The lipidomic architecture of the mouse brain

Lipids are fundamental components of the brain, crucial for synaptic transmission and signal propagation. Altered brain lipid composition is associated with common and rare neuropathologies, yet, the spatial organization of the mammalian brain lipidome remains insufficiently characterized compared to other modalities1-8. Here, we mapped the membrane lipid architecture of the adult mouse brain at micrometric scale, across sexes, and during pregnancy. This Lipid Brain Atlas reveals that lipids define a fine-grained biochemical structure that aligns with functional anatomy. Membrane lipid spatial heterogeneity clusters into territories, which we termed lipizones. Lipizones partially mirror cell type territories, but also capture distal axon terminals. Through lipizones, (i) we reveal the organizing principles of the gray matter lipidome, related to connectivity and cytoarchitecture; (ii) we discover a new axis of oligodendrocyte heterogeneity in the white matter; (iii) and we find biochemical zonation in the choroid plexus and in the ventricular walls. We show that this lipidomic architecture can adapt to changing physiological needs. In the brain of pregnant females, the white matter is metabolically activated and the outer cortex is reorganized. These results are a foundational resource (https://lbae-v2.epfl.ch/), poised to reshape our understanding of lipids in brain development, physiology, and pathology.

systems biology↗

bioRxiv2024

Unified Mass Imaging Maps the Lipidome of Vertebrate Development

Embryo development entails the formation of anatomical structures with distinct biochemical compositions. Compared with the wealth of knowledge on gene regulation, our understanding of metabolic programs operating during embryogenesis is limited. Mass spectrometry imaging (MSI) has the potential to map the distribution of metabolites across embryo development. Here, we established an analytical framework for the joint analysis of large MSI datasets that allows for the construction of multi-dimensional metabolomic atlases. Employing this framework, we mapped the 4D distribution of over a hundred lipids at quasi-single-cell resolution in Danio rerio embryos. We discovered metabolic trajectories that unfold in concert with morphogenesis and revealed spatially organized biochemical coordination overlooked by bulk measurements. Interestingly, lipid mapping revealed unexpected distributions of sphingolipid and triglyceride species, suggesting their involvement in pattern establishment and organ development. Our approach empowers a new generation of whole-organism metabolomic atlases and enables the discovery of spatially organized metabolic circuits.

systems biology↗

bioRxiv2024

Spotiflow: accurate and efficient spot detection for imaging-based spatial transcriptomics with stereographic flow regression

Identifying spot-like structures in large and noisy microscopy images is a crucial step to produce high quality results in various life-science applications. Imaging-based spatial transcriptomics (iST) methods, in particular, critically depend on the precise detection of millions of transcripts in images with low signal-to-noise ratio. Despite advances in computer vision that have revolutionized many biological imaging tasks, currently adopted spot detection techniques are mostly still based on classical signal processing methods that often lack robustness to changing imaging conditions and thus require tedious manual tuning per dataset. In this work, we introduce Spotiflow, a deep learning method that achieves subpixel-accurate localizations by formulating the spot detection task as a multi-scale heatmap and stereographic flow regression problem. Spotiflow can be used for 2D images and 3D volumetric stacks and can be trained to generalize across different imaging conditions, tissue types and chemical preparations, while being substantially more time- and memory-efficient than existing methods. We show the efficacy of Spotiflow via extensive quantitative experiments on a variety of diverse datasets and demonstrate that the enhanced accuracy of SO_SCPLOWPOTIFLOWC_SCPLOW leads to meaningful improvements in the biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow.

bioinformatics↗

bioRxiv2024

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

systems biology↗

bioRxiv2021

Sphingolipid Control of Fibroblast Heterogeneity Revealed by Single-Cell Lipidomics

Human cells produce thousands of lipids that impact a wide range of biological processes in ways we are only starting to characterize. The cellular composition in lipids changes during differentiation events and also varies across individual cells of the same type. Yet, the precise differences in lipid composition that directly affect cell phenotypes remain unknown. Here we have measured the lipidomes and transcriptomes of individual human dermal fibroblasts by coupling high-resolution mass spectrometry imaging to single-cell transcriptomics. We found that the cell-to-cell variation of specific lipid metabolic pathways contributes to the establishment of cell states involved in wound repair and in skin cancer growth. Sphingolipid composition defined fibroblast subpopulations while sphingolipid metabolic rewiring drove cell state transitions. These data uncover a role for cell-to-cell lipid heterogeneity in the determination of cell states and reveal a new regulatory component to the homeostasis and self-organization of multicellular systems.

biochemistry↗

bioRxiv2020

Molecular architecture of the developing mouse brain

The mammalian brain develops through a complex interplay of spatial cues generated by diffusible morphogens, cell-cell interactions, and intrinsic genetic programs that result in the generation of likely more than a thousand distinct cell types. Therefore, a complete understanding of mammalian brain development requires systematic mapping of cell states covering the entire relevant spatiotemporal range. Here we report a comprehensive single-cell transcriptome atlas of mouse brain development spanning from gastrulation to birth. We identified almost a thousand distinct cellular states, including the initial emergence of the neuroepithelium, a rich set of region-specific secondary organizers and a complete developmental program for the functional elements of the brain and its enclosing membranes. We used the atlas to directly test the hypothesis that human glioblastoma reflects a return to a developmental cell state. In agreement, most aneuploid tumor cells matched embryonic rather than adult types, while karyotypically normal cells predominantly matched adult immune cell types.Competing Interest StatementThe authors have declared no competing interest.View Full Text

developmental biology↗