Biology subjects

Sifrim, A.

Publications and source records attributed to Sifrim, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Lack of adoption of computational gene-panel-selection tools in targeted spatial transcriptomics

Targeted spatial transcriptomics requires selecting a gene panel before any experiment is run. Choosing well across tens of thousands of candidate genes under platform-specific probe, panel-size, and sensitivity constraints is difficult to do manually, and impossible to correct once a panel has been used. A growing number of computational tools to automate this selection have been designed, yet it remains unclear whether they are adopted in practice. Several factors could limit uptake: validation uncertainty about panel performance, workflow friction between a ranked gene list and a submission-ready panel, limited platform awareness in tools developed for dissociated single-cell data, and the cost advantage of vendor panels. We analyzed 18 computational tools for gene-panel selection from two angles: their citation landscape and their use across a targeted spatial transcriptomics corpus. Across 540 full-text-classified records, only 35 (6.5%) reported using the cited tool for panel design; most came from computational, benchmark, review, or non-spatial papers, or papers that cited but did not use it. In an independent corpus, only 34 of 479 (7.1%) involved a computational tool, alone or in a hybrid workflow; most used manually curated, premade vendor, or modified vendor panels. Both lines of evidence point the same way: computational selection is rarely part of the reported experimental workflow, and it more often supplemented manual curation than replaced it. Adoption is unlikely to turn on further benchmark gains alone; it will depend on reducing the validation uncertainty and workflow friction that keep users on established panel-design practice.

bioinformatics↗

bioRxiv2026

Modular click-chemistry-based engineering minimally perturbs CRISPR ribonucleoprotein formation for genome imaging applications

CRISPR-Cas9 has become a key genome imaging tool, because of its sequence-specific binding to genomic DNA. To achieve signal amplification or multiplexing, various complex CRISPR-Cas9 modifications were introduced, primarily relying on expression-based delivery. However, these engineering strategies often invoke structural perturbations and their performance remained dictated by vector design and in vivo CRISPR ribonucleoprotein (RNP) assembly. To date, research mainly focused on optimizing the expression-based delivery mechanisms of CRISPR components rather than addressing the engineering itself. Here, we developed a modular SPAAC (bio-orthogonal strain-promoted azide-alkyne cycloaddition) click-chemistry-based strategy for engineering the CRISPR RNP that is completely dissociated from its assembly. We demonstrated that CRISPR can be modified (with fluorophores and DNA) at all stages of RNP in vitro assembly and subsequently validated the technology for genome imaging. Importantly, we also revealed that engineering native CRISPR RNP only after it is being formed, minimizes structural perturbations to the complex and yields a two-fold enhanced accessibility of 'clicked' DNA. Finally, we utilized SPAAC to conjugate non-native RNA-DNA hybrids, regardless of 5'-3' directionality, to develop a covalently-attached rolling-circle signal amplification approach. The modularity and universal nature of our approach holds the potential to deliver significant advances to both CRISPR imaging and prime-editing fields.

bioengineering↗

bioRxiv2026

LipoGrid: A High-Throughput Multi-omics Perturbation Screen Dissects the Genetic Architecture of Lipid Metabolism

Lipids constitute one of the largest and most diverse classes of cellular molecules, sustaining membrane architecture, energy storage, and signaling. Consequently, their dysregulation underlies a broad spectrum of human disease. However, the genetic mechanisms governing lipid homeostasis have remained largely inaccessible, owing to the lack of approaches capable of systematically linking defined genetic perturbations to large-scale changes in cellular lipidome composition. Here we introduce LipoGrid, a spatial mass spectrometry platform that resolves the genetic architecture of lipid metabolism at single-cell resolution. LipoGrid arrays CRISPR/Cas9-perturbed cells on a micropatterned grid and sequentially captures lipidomic and gRNA identity from the same cells, complemented by single-cell RNA sequencing of matched cell populations subjected to the same perturbations. Using this approach, we quantified the relative abundance of 158 distinct lipid species across 143 target genes in a rigorously controlled experimental framework. We find that most gene knockouts produced measurable alterations in lipid composition, often affecting specific lipid classes and molecular subspecies. The screen accurately recapitulated established gene-lipid relationships, including enzyme-substrate specificities, lipid pathway regulators, and disease-associated loss-of-function phenotypes, thereby demonstrating the sensitivity and accuracy of LipoGrid. By jointly profiling transcriptomic and lipidomic responses, we further uncover compensatory feedback mechanisms that buffer the impact of genetic perturbations on the cellular lipidome. Collectively, these findings establish LipoGrid as a scalable multimodal platform for systematically mapping gene-lipid interactions and reveal the regulatory networks linking gene perturbation, transcriptional adaptation, and lipidome remodeling. HighlightsO_LIMicropatterned single-cell growth enables spatial lipidomic perturbation screens C_LIO_LILipoGrid maps 143 gene knockouts to 158 lipid species and transcriptomic states C_LIO_LIPerturbed lipidomes reveal compensatory feedback and lipid-class-specific uptake C_LIO_LIRecovers enzyme substrate specificities and disease-linked lipid signatures C_LI

systems biology↗

bioRxiv2026

FOCUS: end-to-end preprocessing, alignment and resolution-matched integration of spatial multi-omics data

SummaryIntegrating spatial multi-omics data requires coordinated preprocessing, cross-modality alignment and feature registration across modalities that differ in file format, coordinate system and spatial resolution. No existing tool addresses this pipeline end-to-end from raw experimental files till aligned data object. We present FOCUS, an open-source Python package that takes raw data from spatial transcriptomics, mass spectrometry imaging, Raman spectroscopy imaging and brightfield or fluorescence microscopy through modality-specific preprocessing, interactive spatial alignment and resolution-matching registration to a unified MuData object, driven by a single configuration file. Its modular, registry-based architecture allows straightforward extension to additional modalities. FOCUS is accessible via a command-line interface, a browser-based GUI and a Python API. Availability and implementationFOCUS is implemented in Python 3.11, with a browser-based GUI built on a Vue.js 3 frontend served by a Flask backend. Source code, documentation and container recipes are available at https://github.com/sifrimlab/FOCUS; a versioned release is archived on Zenodo (10.5281/zenodo.21700038). Outputs use the AnnData and MuData formats and are directly compatible with the scverse ecosystem.

bioinformatics↗

bioRxiv2026

SubCellSpace: Automated characterization of subcellular mRNA localization patterns in spatial transcriptomics

The localized translation of transcripts is a universal phenomenon across biological domains. Many examples of subcellular RNA localization and their functional importance have been described. However, these examples remain anecdotal, and a more systematic genome and cell-type-wide analysis is needed. Current spatial transcriptomic techniques can characterize hundreds to thousands of transcript species at subcellular resolutions, enabling the large-scale investigation of subcellular mRNA localization. Here we describe SubCellSpace, a computational framework to learn general representations of mRNA localization patterns. By embedding observed single-cell subcellular localization patterns (SLPs) to an interpretable latent space, SubCellSpace can detect and statistically infer the presence of SLPs, uncover colocalizing gene-pairs and characterize cellular heterogeneity for pattern-presentation. We benchmark SubCellSpace in both synthetic and real data, showing it can correctly detect previously described apical/basal polarized genes in the enterocytes of mouse small-intestine, as well as encode the enterocytes orientation. Additionally, we provide a tailored spatial transcriptomics validation dataset for benchmarking SLP identification based on transcripts previously described to be enriched near subcellular structures in HEK293T cells. We propose a practical and computationally-efficient classification workflow that automatically detects localized transcript species and quantifies their degree of patterning, while controlling false positive rates. Finally, we showcase SubCellSpace in both supervised and unsupervised settings, to either classify pre-determined SLPs or to explore spatial patterning without specifying pattern types a priori. Automated AI models such as SubCellSpace and their integration in spatial transcriptomics analysis workflows will help characterize previously undiscovered subcellular RNA localization phenomena, providing novel insights into post-transcriptional regulation mechanisms.

bioinformatics↗

bioRxiv2025

Motor System Oligodendroglia Atlas Reveals Activation States Associated with Region-Specific Vulnerability in ALS

Oligodendrocytes, the second most abundant cell type in the motor cortex and the most abundant in the spinal cord--are increasingly recognized as active contributors to neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Yet their diversity and disease relevance in the central nervous system remain incompletely defined. Here, we generate a comprehensive single-nucleus transcriptomic atlas of 176 samples, integrating 2 in-house and 16 publicly available datasets. It encompasses more than 1,000,000 cells (350,000 oligodendrocytes and oligodendrocyte precursors) from the human motor cortex and spinal cord in non-neurological controls and ALS patients. We identify a previously unrecognized oligodendrocyte subpopulation--marked by PPEF1--and uncover regional differences, with distinct OPALIN+ and RBFOX1+ oligodendrocyte proportions between cortex and spinal cord. Importantly, we demonstrate that "disease-associated oligodendrocytes" (DAOs) represent a distinct activation state, rather than a distinct subpopulation. In ALS, spinal cord oligodendrocytes selectively shift toward reactive states, characterized by JUND-enriched regulon activity, heightened metabolic demand, increased intercellular signaling, and activation of cell-death pathways. Finally, we identify LRP2 as a novel transcriptional oligodendrocyte marker and find LRP2 and MBP proteins to be elevated in ALS cerebrospinal fluid (CSF), offering a potential biomarker of oligodendrocyte dysfunction. Our study provides a systems-level framework to interpret oligodendrocyte and OPC changes in ALS, linking cell state shifts to molecular alterations that can guide biomarker and therapeutic strategies.

neuroscience↗

bioRxiv2025

Multi-omics integration and batch correction using a modality-agnostic deep learning framework

State-of-the-art biotechnologies allow the detection of different molecular species on the same biological sample, generating complex highly-dimensional multi-modal datasets. Gaining a holistic understanding of biological phenomena, such as oncogenesis or aging, requires integrating these diverse modalities into low-dimensional data representations while correcting for technical artifacts. Here we present MIMA, a modular, unsupervised AI framework for multi-omics data integration and batch correction. Applied to complex spatial and single-cell datasets, MIMA effectively removes batch effects, while preserving biologically relevant information, and learns representations predictive of expert pathologist annotations. Additionally, it enables cross-modal translation, uncovers molecular patterns not captured by manual annotations, and despite being modality-agnostic performs on par with specialized state-of-the-art tools. MIMAs flexibility and scalability make it a powerful tool for multimodal data analysis. MIMA provides a foundation for AI-based, multi-omics augmented digital pathology frameworks, offering new opportunities for improved patient stratification and precision medicine through the comprehensive integration of high-dimensional molecular data and histopathological imaging.

bioinformatics↗

bioRxiv2025

Spatial epigenomic niches underlie glioblastoma cell state plasticity

IDH-wildtype glioblastoma (GBM) is an aggressive brain tumor with poor survival and few therapeutic options. Transcriptionally-defined cell states coexist in GBM and occupy defined regions of the tumor. Evidence indicates that GBM cell states are plastic, but it remains unclear if they are determined by the underlying epigenetic state and/or by microenvironmental factors. Here, we present spatially-resolved epigenomic profiling of human GBM tissues that implicate chromatin structure as a key enabler of cell plasticity. We report two epigenetically-defined and spatially-nested tumor niches. Each niche activates short-range molecular signals to maintain its own state and, surprisingly, long-range signals to reinforce the state of the neighboring niche. The position of a cell along this gradient-like system of opposing signals determines its likelihood to be in one state or the other. Our results reveal an intrinsic system for cell plasticity that is encoded in the chromatin profiles of two adjacent niches that dot the topological architecture of GBM in cartesian space.

genomics↗

bioRxiv2025

Alzheimer's disease and its co-pathologies: implications for hippocampal degeneration, cognitive decline, and the role of APOE ϵ4

INTRODUCTIONIn neurodegenerative dementias, the co-occurrence and interaction of A{beta}, tau, and other pathological lesions confound their individual contributions to neurodegeneration and their modulation by risk factors. METHODSWe analyzed 480 post-mortem human brains (ages 50-99) using regression and structural equation models to assess the relationships among A{beta}, tau, LATE-NC, -synuclein, other age-related lesions, and APOE {varepsilon}4, as well as their effects on CA1 neuronal density, brain weight, and cognitive status. RESULTSA{beta}, tau, LATE-NC, and amygdala-predominant -synuclein pathology were highly interconnected. Tau was the strongest predictor of global neurodegeneration, while LATE-NC primarily, but not exclusively, affected hippocampal neuron loss. Small vessel disease correlated with both LATE-NC and -synuclein, while APOE {varepsilon}4 was mainly associated with extracellular and capillary A{beta}. DISCUSSIONAlthough Alzheimers pathology plays a central role in brain degeneration, coexisting pathologies can both exacerbate and independently contribute to it. These factors should be considered in patient stratification.

neuroscience↗

bioRxiv2025

Defining the role of fibroblasts in skin expansion

Stretch-mediated tissue expansion is commonly used to grow extra skin for reconstructive surgeries. To ensure harmonious growth, the two main skin compartments, the epidermis and the dermis, must both expand in a coordinated manner. Although the epidermal response has been previously described, it remains unclear how fibroblasts, the main supporting cell type, respond to stretching in vivo. Here we map the transcriptional response of the entire skin during stretch-mediated tissue expansion, and we describe the fibroblast response to stretching in vivo. We show an increase in fibroblast volume accompanied by changes in organisation. We demonstrate that stretching forces fibroblasts to exit their quiescent state and restart proliferation. Simultaneously, fibroblasts decrease their collagen content and increase the expression of specific extracellular matrix remodelling factors. By combining data from the in vivo stretching model and an in vitro keratinocyte-fibroblast co-culture system, we demonstrate that changes in fibroblasts promote the self-renewal of the epidermal stem cells, thus coordinating the response of these two compartments during skin expansion. These findings provide valuable insights to guide the design of in vivo stretch-mediated tissue expansion protocols and the production of in vitro skin grafts for clinical application.

cell biology↗

bioRxiv2024

Single cell transcriptomics reveal mast cell heterogeneity in the human gut

Gut mast cells are key players in infection and inflammation, as well as in homeostasis. Mast cells regulate gastrointestinal (GI) function by controlling vascular and epithelial permeability, and interacting with the immune system and the enteric nervous system. Mucosal (MCT) and connective tissue (MCTC) mast cells coexist in the gastrointestinal tract, but are located in distinct microanatomical niches. However, little is known about the transcriptional heterogeneity of human intestinal mast cells. Additionally, whether distinct microanatomical niches instruct mast cell phenotypes in the human gut is currently unknown. We therefore performed 10x single cell RNA sequencing on healthy rectal biopsies and on dissected layers of human sigmoid colon. We identified five transcriptionally distinct mast cell subsets (MC1-5) which exhibited a layer-specific distribution in the healthy human colon and distinct cytokine, chemokine, protease, and transcription factor profiles, and were associated with putative immune and neuro-immune functions. This study provides a framework for the integration of mast cell heterogeneity in the study of gastrointestinal disease mechanisms in humans.

immunology↗

bioRxiv2023

Integration of Multiple Spatial-Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

cancer biology↗

bioRxiv2023

A multi-omics genome-and-transcriptome single-cell atlas of human preimplantation embryogenesis reveals the cellular and molecular impact of chromosome instability

The frequent acquisition of genomic abnormalities in human preimplantation embryos is a leading cause of pregnancy loss, but does not necessarily prohibit healthy offspring. However, the impact of genomic abnormalities on cellular states and development of the early human embryo remains largely unclear. Here, we characterise aneuploidy and reconstruct gene regulatory networks in human preimplantation embryos, and investigate gene expression and developmental perturbations instigated by aneuploidy using single-cell genome-and-transcriptome sequencing (G&T-seq). At the genomic level, we show that acquired numerical and structural chromosomal aberrations are frequent across all stages of early embryogenesis and in all cell lineages. At the transcriptome level, we identify regulators of cell identity and uncover a network of 248 transcription factors from 10 major gene regulatory modules that characterise the distinct lineages of human preimplantation embryos. By integrating single-cell DNA-with RNA-information, we unveil how expression levels are affected by losses or gains of the corresponding genes in embryonic cells across human preimplantation development, as well as how copy-number aberrant transcription factor genes perturb the expression of their cognate target genes in euploid regions. Furthermore, we reveal a majority of aneuploid cells show a developmental delay and reduced fitness, indicating cell competition within the mosaic diploid-aneuploid embryo, which may contribute to selection against aneuploid cells and the birth of healthy offspring from mosaic diploid-aneuploid embryos. In summary, our multi-modal analyses provide unprecedented insights into early human embryo development.

developmental biology↗

bioRxiv2023

Single-cell Genome-and-Transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones

Single-cell multi-omics methods are enabling the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at both the single-cell and pseudo-bulk level when compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited resolution and accuracy, underlining the importance of affordable multi-omic approaches. Moreover, applying Gtag&T-seq to a melanoma xenograft model before treatment and at minimal residual disease revealed differential cell state plasticity and treatment response between cancer subclones. In summary, Gtag&T-seq is a low-cost and accurate single-cell multi-omics method enabling the exploration of genetic alterations and their functional consequences in single cells at scale.

genomics↗

bioRxiv2022

Neuro-immune Crosstalk in the Enteric Nervous System from Early Postnatal Development to Adulthood

Correct development and maturation of the enteric nervous system (ENS) is critical for survival. Early in life, the ENS requires significant refinement in order to adapt to the evolving needs of the tissue, changing from milk to solid food at the time of weaning. Here, we demonstrate that resident macrophages of the muscularis externa, MM{phi}, refine the ENS early in life by pruning synapses and phagocytosing abundant enteric neurons. After weaning, MM{phi} continue to closely interact with the ENS, acquire a microglia-like phenotype and are crucial for the survival of enteric neurons. Of note, this microglia-like phenotype is instructed by TGF{beta} produced by the ENS, introducing a novel reciprocal cell-cell communication responsible for the maintenance of the neuron-associated MM[FE] niche in the gut. These findings elucidate a novel role of intestinal macrophages in ENS refinement early in life, and open new opportunities to treat intestinal neurodegenerative disorders by manipulating the ENS-macrophage niche.

immunology↗

bioRxiv2020

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease

Congenital Heart Disease (CHD) affects approximately 7-9 children per 1000 live births. Numerous genetic studies have established a role for rare genomic variants at the copy number variation (CNV) and single nucleotide variant level. In particular, the role of de novo mutations (DNM) has been highlighted in syndromic and non-syndromic CHD. To identify novel haploinsufficient CHD disease genes we performed an integrative analysis of CNVs and DNMs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm (TAA). We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed mutation rate testing for DNMs identified in 2,489 parent-offspring trios. Our combined analysis revealed 21 genes which were significantly affected by rare genomic deletions and/or constrained non-synonymous de novo mutations in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in singletons and small cases series, or show new associations with CHD. In addition, a systems level analysis revealed shared contribution of CNV deletions and DNMs in CHD probands, affecting protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes.

genetics↗

bioRxiv2019

Systems genetics analysis identify calcium signalling defects as novel cause of congenital heart disease

BackgroundCongenital heart disease (CHD) occurs in almost 1% of newborn children and is considered a multifactorial disorder. CHD may segregate in families due to significant contribution of genetic factors in the disease aetiology. The aim of the study was to identify pathophysiological mechanisms in families segregating CHD. MethodsWe used whole exome sequencing to identify rare genetic variants in ninety consenting participants from 32 Danish families with recurrent CHD. We applied a systems biology approach to identify developmental mechanisms influenced by accumulation of rare variants. We used an independent cohort of 714 CHD cases and 4922 controls for replication and performed functional investigations using zebrafish as in vivo model. ResultsWe identified 1,785 genes, in which rare alleles were shared between affected individuals within a family. These genes were enriched for known cardiac developmental genes and 218 of the genes were mutated in more than one family. Our analysis revealed a functional cluster, enriched for proteins with a known participation in calcium signalling. Replication confirmed increased mutation burden of calcium-signalling genes in CHD patients. Functional investigation of zebrafish orthologues of ITPR1, PLCB2 and ADCY2 verified a role in cardiac development and suggests a combinatorial effect of inactivation of these genes. ConclusionsThe study identifies abnormal calcium signalling as a novel pathophysiological mechanism in human CHD and confirms the complex genetic architecture underlying CHD.

genetics↗

bioRxiv2019

HiVA: an integrative wet- and dry-lab platform for haplotype and copy number analysis of single-cell genomes

Haplotyping is imperative for comprehensive analysis of genomes, imputation of genetic variants and interpretation of error-prone single-cell genomic data. Here we present a novel sequencing-based approach for whole-genome SNP typing of single cells, and determine genome-wide haplotypes, the copy number of those haplotypes as well as the parental and segregational origin of chromosomal aberrations from sequencing- and array-based SNP landscapes of single cells. The analytical workflow is made available as an interactive web application HiVA (https://hiva.esat.kuleuven.be).

genomics↗