Biology subjects

Kats, I.

Publications and source records attributed to Kats, I..

9 recordsLinked to original sources

bioRxiv2025

MOFA-FLEX: A Factor Model Framework for Integrating Omics Data with Prior Knowledge

Latent factor models are first-line analysis approaches for single- and multi-omics data, essential for data integration, alignment, and biological signal discovery. To cater for new technologies and experimental designs, bespoke extensions of factor models have been proposed, incorporating spatial structure, temporal dynamics and the noise characteristics of single-cell assays. However, the development of tailored methods and software for individual use cases is laborious and requires advanced statistical and domain expertise, posing a significant barrier to users. To address this, we here propose MOFA-FLEX, a flexible and modular factor analysis framework designed for customisable modelling across diverse multi-omics data scenarios. Built on probabilistic programming, MOFA-FLEX unifies previously isolated extensions of factor analysis - including flexible priors, non-negativity constraints, supervision signals, and alternative data likelihoods - allowing models to be configured declaratively without requiring manual engineering. Additionally, MOFA-FLEX features a novel domain knowledge module to inform and connect latent factors to gene programs. We demonstrate MOFA-FLEX across multiple applications, showing (i) improved robustness in recovering gene programs from noisy prior knowledge in scRNA-seq data; (ii) effective disentanglement of technical and biological variation in multi-omic CITE-seq; and (iii) tailored spatial modelling that reveals spatially organised disease-associated gene programs in breast cancer.

bioinformatics↗

bioRxiv2023

Spatial and temporal transcriptomics of SHH-medulloblastoma with chromothripsis identifies multiple genetic clones that resist to treatment and lead to relapse

Paediatric medulloblastomas with chromothripsis are characterised by high genomic instability and are among the tumours with the worst prognosis. However, the determinants of their aggressiveness and the molecular makeup of chromothriptic medulloblastoma are not well understood. Here, we applied spatial transcriptomics to profile a cohort of 13 chromothriptic and non-chromothriptic medulloblastomas from the same molecular subgroup. Our data reveal a higher extent of spatial intra-tumour heterogeneity in chromothriptic medulloblastomas, which is associated with increased proliferation and stemness, but lower immune infiltration and differentiation. Spatial mapping of genetic subclones of the same tumour identify a regionally distinct architecture and clone-specific phenotypic features, with distinct degrees of differentiation, proliferation and immune infiltration between clones. We conducted temporal profiling of 11 patient-derived xenografts from chromothriptic medulloblastomas, covering the transition from the minimal residual disease stage to treatment-resistant regrown tumours. In chromothriptic medulloblastoma, an ecosystem of cells from multiple genetic clones resisting treatment and leading to relapse highlighted the importance of multi-clone interplay. Finally, we identified a potential role for tumour microtubes in treatment resistance in chromothriptic medulloblastoma, suggesting cell network communication as a putative target. HighlightsO_ST_ABSBiological insightsC_ST_ABSO_LIMedulloblastomas with chromothripsis are characterised by higher spatial intra-tumour heterogeneity, proliferation and stemness, but lower immune infiltration and differentiation, as compared with non-chromothriptic medulloblastomas C_LIO_LISpatially aware genetic clone assignment identifies phenotypic features such as degree of differentiation, proliferation and immune infiltration enriched in specific clones C_LIO_LICells from distinct genetic clones resist to treatment and give rise to relapse in patient-derived xenografts of medulloblastoma with chromothripsis C_LIO_LITumour microtubes point to putative molecular processes implicated in treatment resistance in chromothriptic medulloblastoma C_LI Technological advancesO_LIValidation of the spatial representation of patient-derived xenograft models, providing novel insights into the faithfulness of such models to study cancer C_LIO_LISpatial mapping of clones based on spatial transcriptomics and copy-number variant profiling C_LI

cancer biology↗

bioRxiv2022

Spatially resolved single-cell multiomics map of human trophoblast differentiation in early pregnancy

The relationship between the human placenta, the extraembryonic organ built by the fetus, and the decidua, the mucosal layer of the uterus, is essential to nurture and protect the fetus during pregnancy. Extravillous trophoblast cells (EVTs) anchor the placenta and infiltrate the decidua, transforming the maternal arteries into high conductance vessels. Defects in trophoblast invasion and arterial transformation established during early pregnancy underlie common pregnancy disorders such as pre-eclampsia. Despite its importance, how EVT invasion is regulated in humans is still unclear due the inaccessibility of the entire pregnant uterus and, until recently, a lack of reliable in vitro models. Here, we have generated a spatially-resolved multiomics single-cell atlas of the entire maternal-fetal interface including the myometrium, allowing us to resolve the full trajectory of trophoblast differentiation. We have used this cellular map to elucidate the main regulatory programmes mediating EVT invasion and show that they are preserved in trophoblast organoids. We define the transcriptomes of the final cell states of trophoblast invasion: placental bed giant cells (fused multinucleated EVTs) and endovascular EVTs (which form plugs inside the maternal arteries). We reconstruct the cell-cell communication events contributing to trophoblast invasion and GC formation, and define the dual role of interstitial EVTs and endovascular EVTs in mediating arterial transformation during early pregnancy. Together, our data provides a comprehensive analysis of postimplantation trophoblast differentiation in humans that can be used as a blueprint to design accurate multilineage placental in vitro models.

genomics↗

bioRxiv2021

SpatialDE2: Fast and localized variance component analysis of spatial transcriptomics

Spatial transcriptomics is now a mature technology, allowing to assay gene expression changes in the histological context of complex tissues. A canonical analysis workflow starts with the identification of tissue zones that share similar expression profiles, followed by the detection of highly variable or spatially variable genes. Rapid increases in the scale and complexity of spatial transcriptomic datasets demand that these analysis steps are conducted in a consistent and integrated manner, a requirement that is not met by current methods. To address this, we here present SpatialDE2, which unifies the mapping of tissue zones and spatial variable gene detection as integrated software framework, while at the same time advancing current algorithms for both of these steps. Formulated in a Bayesian framework, the model accounts for the Poisson count noise, while simultaneously offering superior computational speed compared to previous methods. We validate SpatialDE2 using simulated data and illustrate its utility in the context of two real-world applications to the spatial transcriptomics profiles of the mouse brain and human endometrium.

bioinformatics↗

bioRxiv2021

Muon: multimodal omics analysis framework

Advances in multi-omics technologies have led to an explosion of multimodal datasets to address questions ranging from basic biology to translation. While these rich data provide major opportunities for discovery, they also come with data management and analysis challenges, thus motivating the development of tailored computational solutions to deal with multi-omics data. Here, we present a data standard and an analysis framework for multi-omics -- MUON -- designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible data structure, supporting an arbitrary number of omics layers. The MUON data structure is interoperable with existing community standards for single omics, and it provides easy access to both data from individual omics as well as multimodal dataviews. Building on this data infrastructure, MUON enables a versatile range of analyses, from data preprocessing, the construction of multi-omics containers to flexible multi-omics alignment.

genomics↗

bioRxiv2020

Mechanisms of up-regulation of Ubiquitin-Proteasome activity in the absence of NatA dependent N-terminal acetylation

N-terminal acetylation is a prominent protein modification and inactivation of N-terminal acetyltransferases (NATs) cause protein homeostasis stress. Using multiplexed protein stability (MPS) profiling with linear ubiquitin fusions as reporters for the activity of the ubiquitin proteasome system (UPS) we observed increased UPS activity in NatA, but not NatB or NatC mutants. We find several mechanisms contributing to this behavior. First, NatA-mediated acetylation of the N-terminal ubiquitin independent degron regulates the abundance of Rpn4, the master regulator of the expression of proteasomal genes. Second, the abundance of several E3 ligases involved in degradation of UFD substrates is increased in cells lacking NatA. Finally, we identify the E3 ligase Tom1 as a novel chain elongating enzyme (E4) involved in the degradation of linear ubiquitin fusions via the formation of branched K11 and K29 ubiquitin chains, independently of the known E4 ligases involved in UFD, leading to enhanced ubiquitination of the UFD substrates.

cell biology↗

bioRxiv2019

Translational regulation of Pmt1 and Pmt2 by Bfr1 affects unfolded protein O-mannosylation

O-mannosylation is implicated in protein quality control in Saccharomyces cerevisiae due to the attachment of mannose to serine and threonine residues of un- or misfolded proteins in the endoplasmic reticulum (ER). This process also designated as unfolded protein O-mannosylation (UPOM) that ends futile folding cycles and saves cellular resources is mainly mediated by protein O-mannosyltransferases Pmt1 and Pmt2. Here we describe a genetic screen for factors that influence O-mannosylation in yeast, using slow-folding GFP as a reporter. Our screening identifies the RNA binding protein brefeldin A resistance factor 1 (Bfr1) that has not been linked to O-mannosylation and ER protein quality control before. We find that Bfr1 affects O-mannosylation through changes in Pmt1 and Pmt2 protein abundance, but has no effect on PMT1 and PMT2 transcript levels, mRNA localization to the ER membrane or protein stability. Ribosome profiling reveals that Bfr1 is a crucial factor for Pmt1 and Pmt2 translation thereby affecting unfolded protein O-mannosylation. Our results uncover a new level of regulation of protein quality control in the secretory pathway.

molecular biology↗

bioRxiv2018

Bicoid gradient formation mechanism and dynamics revealed by protein lifetime analysis

Embryogenesis relies on instructions provided by spatially organized signaling molecules known as morphogens. Understanding the principles behind morphogen distribution and how cells interpret locally this information remains a major challenge in developmental biology. Here we introduce morphogen-age measurements as a novel approach to retrieve key parameters in morphogen dynamics. Using a tandem fluorescent timer (tFT) as a protein-age sensor we find a gradient of increasing age of Bicoid (Bcd) along the anterior-posterior (AP) axis in the early Drosophila embryo. Quantitative analysis retrieves parameter that are most consistent with the synthesis-diffusion-degradation (SDD) model underlying Bcd-gradient formation, and rule out some other hypotheses for gradient formation. Moreover, we show that the timer can detect transitions in the dynamics associated with syncytial cellularization. Our results provide new insight into Bcd gradient formation, and demonstrate how morphogen age-information can complement knowledge about movement, abundance and distribution, which should be widely applicable for other systems.

developmental biology↗

bioRxiv2017

A genome-wide resource for high-throughput genomic tagging of yeast ORFs

Here we describe a C-SWAT library for high-throughput tagging of Saccharomyces cerevisiae ORFs. It consists of 5661 strains with an acceptor module inserted after each ORF, which can be efficiently replaced with tags or regulatory elements. We validate the library with targeted sequencing and demonstrate its use by tagging the yeast proteome with bright fluorescent proteins, determining how sequences downstream of ORFs influence protein expression and localizing previously undetected proteins.

genomics↗