Biology subjects

Karaiskos, N.

Publications and source records attributed to Karaiskos, N..

14 recordsLinked to original sources

bioRxiv2026

pyTrance finds co-localizing RNAs in subcellular spatial transcriptomics data

Regulation of RNA subcellular localization is crucial for cellular functions in health and disease. For example, local translation of co-localized RNAs is crucial for neural biology. However, it is challenging to identify RNA co-localization events. Here, we present pyTrance, a computational framework that predicts and quantifies subcellular RNA co-localization from spatial transcriptomics data, leveraging latent embeddings learned by a graph neural network. Based on extensive benchmarking, detection of co-localizing RNAs was more accurate and robust compared to existing methods. In mouse brain tissue, pyTrance found several RNA co-localization patterns. Co-localized RNAs were often functionally related and validated by biological knowledge. Interestingly, among novel patterns, pyTrance identified co-localization of GABAergic markers, including Gad1, in neuronal projections. Experimental validation led to the discovery of a spatial overlap between Gad1 mRNA/protein, strongly suggesting local translation. Our results establish pyTrance as a state-of-the-art method to discover biologically important RNA co-localization at subcellular resolution.

bioinformatics↗

bioRxiv2025

Spatiotemporal dynamics of tumor microenvironment remodelling

During tumorigenesis, interactions between tumor and stromal cells progressively remodel the tumor microenvironment (TME) towards pro-tumoral functions. Understanding early TME remodeling dynamics is therefore crucial for developing interceptive therapies. However, clinical samples typically provide isolated, late tumorigenesis snapshots. To overcome this limitation, we generated triple-negative breast cancer mice that develop multifocal, asynchronous tumors along a continuous luminal-to-basal transdifferentiation trajectory. Ordering spatial transcriptomes from 100+ ducts along this trajectory reveals the spatiotemporal dynamics of TME remodeling and underlying molecular mechanisms. Cancer-associated myofibroblasts (myCAFs) emerge as key players in advanced tumors, where they orchestrate pro-invasive remodeling of the tumor-stromal interface. myCAFs are conserved in patient-derived xenograft models and steer tumor trajectories towards invasive phenotypes when co-injected with tumor cells in syngeneic mice. Our study shows that temporal ordering of spatially-resolved disease snapshots unravels some of the molecular "forces" that, starting from the cell-of-origin, propel cells/microenvironments along a disease trajectory.

cancer biology↗

bioRxiv2023

Open-ST: High-resolution spatial transcriptomics in 3D

Spatial transcriptomics (ST) methods have been developed to unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary tumor and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal and tumor populations in space. Distinct cell states were organized around cell-cell communication hotspots in the tumor, but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. We anticipate Open-ST to accelerate the identification of spatial molecular mechanisms in 2D and 3D.

systems biology↗

bioRxiv2023

High-resolution molecular atlas of a lung tumor in 3D

Cells live and interact in three-dimensional (3D) cellular neighborhoods. However, histology and spatial omics methods mostly focus on 2D tissue sections. Here we present a 3D spatial atlas of a routine clinical sample, an aggressive human lung carcinoma, by combining in situ quantification of 960 cancer-related genes across [~]340,000 cells with measurements of tissue-mechanical components. 3D cellular neighborhoods subdivided the tumor microenvironment into tumor, stromal, and immune multicellular niches. Interestingly, pseudotime analysis suggested that pro-invasive epithelial-to-mesenchymal transition (EMT), detected in stroma-infiltrating tumor cells, already occurred in one region at the tumor surface. There, myofibroblasts and macrophages specifically co-localized with pre-invasive tumor cells and their multicellular molecular signature identified patients with shorter survival. Moreover, cytotoxic T-cells did not infiltrate this niche but colocalized with inhibitory dendritic and regulatory T cells. Importantly, systematic scoring of cell-cell interactions in 3D neighborhoods highlighted niche-specific signaling networks accompanying tumor invasion and immune escape. Compared to 2D, 3D neighborhoods improved the characterization of immune niches by identifying dendritic niches, capturing the 3D extension of T-cell niches and boosting the quantification of niche-specific cell-cell interactions, including druggable immune checkpoints. We believe that 3D communication analyses can improve the design of clinical studies investigating personalized, combination immuno-oncology therapies.

systems biology↗

bioRxiv2022

Optocoder: computational decoding of spatially indexed bead arrays

MotivationAdvancing technologies that quantify gene expression in space are transforming contemporary biology research. A class of spatial transcriptomics methods uses barcoded bead arrays that are optically decoded via microscopy and are later matched to sequenced data from the respective libraries. To obtain a detailed representation of the tissue in space, robust and efficient computational pipelines are required to process microscopy images and accurately basecall the bead barcodes. ResultsOptocoder is a computational framework that processes microscopy images to decode bead barcodes in space. It efficiently aligns images, detects beads, and corrects for confounding factors of the fluorescence signal, such as crosstalk and phasing. Furthermore, Optocoder employs supervised machine learning to strongly increase the number of matches between optically decoded and sequenced barcodes. We benchmark Optocoder using data from an in-house spatial transcriptomics platform, as well as from Slide-Seq(V2), and we show that it efficiently processes all datasets without modification. AvailabilityOptocoder is publicly available, open-source and provided as a stand-alone Python package on GitHub: https://github.com/rajewsky-lab/optocoder

bioinformatics↗

bioRxiv2021

Transcriptomic responses of the human kidney to acute injury at single cell resolution

BackgroundAcute kidney injury (AKI) occurs frequently in critically ill patients and is associated with adverse outcomes. Cellular mechanisms underlying AKI and kidney cell responses to injury remain incompletely understood. MethodsWe performed single-nuclei transcriptomics, bulk transcriptomics, molecular imaging studies, and conventional histology on kidney tissues from 8 individuals with severe AKI (stage 2 or 3 according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria). Specimens were obtained within 1-2 hours after individuals had succumbed to critical illness associated with respiratory infections, with 4 of 8 individuals diagnosed with COVID-19. Control kidney tissues were obtained post-mortem or after nephrectomy from individuals without AKI. ResultsHigh-depth single cell-resolved gene expression data of human kidneys affected by AKI revealed enrichment of novel injury-associated cell states within the major cell types of the tubular epithelium, in particular in proximal tubules, thick ascending limbs and distal convoluted tubules. Four distinct, hierarchically interconnected injured cell states were distinguishable and characterized by transcriptome patterns associated with oxidative stress, hypoxia, interferon response, and epithelial-to-mesenchymal transition, respectively. Transcriptome differences between individuals with AKI were driven primarily by the cell type-specific abundance of these four injury subtypes rather than by private molecular responses. AKI-associated changes in gene expression between individuals with and without COVID-19 were similar. ConclusionThe study provides an extensive resource of the cell type-specific transcriptomic responses associated with critical illness-associated AKI in humans, highlighting recurrent disease-associated signatures and inter-individual heterogeneity. Personalized molecular disease assessment in human AKI may foster the development of tailored therapies.

molecular biology↗

bioRxiv2021

Image-based representation of massive spatial transcriptomics datasets

We present STIM, an imaging-based computational framework focused on visualizing and aligning high-throughput spatial sequencing datasets. STIM is built on the powerful, scalable ImgLib2 and BigDataViewer (BDV) image data frameworks and thus enables novel development or transfer of existing computer vision techniques to the sequencing domain characterized by datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIMs capabilities by representing, interactively visualizing, 3D rendering, automatically registering and segmenting publicly available spatial sequencing data from 13 serial sections of mouse brain tissue, and from 19 sections of a human metastatic lymph node.

bioinformatics↗

bioRxiv2021

Spacemake: processing and analysis of large-scale spatial transcriptomics data

Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques can read mRNA expression levels from tissue sections and at the same time register information about the original locations of the molecules in the tissue. The resulting datasets are processed and analyzed by accompanying software which, however, is incompatible across inputs from different technologies. Here, we present spacemake, a modular, robust and scalable spatial transcriptomics pipeline built in snakemake and python. Spacemake is designed to handle all major spatial transcriptomics datasets and can be readily configured to run on other technologies. It can process and analyze several samples in parallel, even if they stem from different experimental methods. Spacemakes unified framework enables reproducible data processing from raw sequencing data to automatically generated downstream analysis reports. Moreover, spacemake is built with a modular design and offers additional functionality such as sample merging, saturation analysis and analysis of long-reads as separate modules. Moreover, spacemake employs novoSpaRc to integrate spatial and single-cell transcriptomics data, resulting in increased gene counts for the spatial dataset. Spacemake is open-source, extendable and can be readily integrated with existing computational workflows.

bioinformatics↗

bioRxiv2021

The neuroinflammatory interleukin-12 signaling pathway drives Alzheimer's disease-like pathology by perturbing oligodendrocyte survival and neuronal homeostasis

Alzheimers disease (AD) is characterized by deposition of pathological amyloid-{beta} (A{beta}) and tau protein aggregates and involves chronic neuroinflammation, ultimately leading to neurodegeneration and cognitive decline. Central in AD-related neuroinflammation is the proinflammatory interleukin-12 (IL-12)/IL-23 signaling pathway whose inhibition has been shown to attenuate pathology and cognitive defects in AD-like mice. In order to explore which cell types are involved in this neuroinflammatory cascade, we used single-nuclei RNA sequencing in AD-like APPPS1 mice lacking or harboring IL-12/IL-23 signaling. We found Il12b transcripts encoding the common p40 subunit of IL-12/IL-23 signaling to be expressed preferentially, but not exclusively, in microglia in an AD-specific manner. In contrast, transcripts for the other subunits of the IL-12 signaling pathway were expressed constitutively in neurons and oligodendrocytes irrespective of AD pathology, while transcripts for IL-23 were almost undetectable. Notably, genetic ablation of IL-12/IL-23 signaling did not affect the inflammatory gene expression profile of the AD-specific disease associated microglia (DAM), but reversed the loss of mature myelin-producing oligodendrocytes and alterations in neuronal homeostasis in APPPS1 mice. Taken together, our results reveal that IL-12, but not IL-23 is the main driver of AD-specific IL-12/IL-23 neuroinflammation, which alters neuronal and oligodendrocyte functions. Given that drugs targeting IL-12 already exist, our data may foster first clinical trials in AD subjects using this novel neuroimmune target.

neuroscience↗

bioRxiv2019

Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

Single-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

systems biology↗

bioRxiv2019

Full-length mRNA sequencing reveals principles of poly(A) tail length control

Although mRNAs are key molecules for understanding life, there exists no method to determine the full-length sequence of endogenous mRNAs including their poly(A) tails. Moreover, although poly(A) tails can be modified in functionally important ways, there also exists no method to accurately sequence them. Here, we present FLAM-seq, a rapid and simple method for high-quality sequencing of entire mRNAs. We report a cDNA library preparation method coupled to single-molecule sequencing to perform FLAM-seq. Using human cell lines, brain organoids, and C. elegans we show that FLAM-seq delivers high-quality full-length mRNA sequences for thousands of different genes per sample. We find that (a) 3 UTR length is correlated with poly(A) tail length, (b) alternative polyadenylation sites and alternative promoters for the same gene are linked to different tail lengths, (c) tails contain a significant number of cytosines. Thus, we provide a widely useful method and fundamental insights into poly(A) tail regulation.

systems biology↗

bioRxiv2018

Charting a tissue from single-cell transcriptomes

Massively multiplexed sequencing of RNA in individual cells is transforming basic and clinical life sciences. However, in standard experiments, tissues must first be dissociated. Thus, after sequencing, information about the spatial relationships between cells is lost although this knowledge is crucial for understanding cellular and tissue-level function. Recent attempts to overcome this fundamental challenge rely on employing additional in situ gene expression imaging data which can guide spatial mapping of sequenced cells. Here we present a conceptually different approach that allows to reconstruct spatial positions of cells in a variety of tissues without using reference imaging data. We first show for several complex biological systems that distances of single cells in expression space monotonically increase with their physical distances across tissues. We therefore seek to map cells to tissue space such that this principle is optimally preserved, while matching existing imaging data when available. We show that this optimization problem can be cast as a generalized optimal transport problem and solved efficiently. We apply our approach successfully to reconstruct the mammalian liver and intestinal epithelium as well as fly and zebrafish embryos. Our results demonstrate a simple spatial expression organization principle and that this principle (or future refined principles) can be used to infer, for individual cells, meaningful spatial position probabilities from the sequencing data alone.

systems biology↗

bioRxiv2017

The Drosophila Embryo at Single Cell Transcriptome Resolution

Drosophila is a premier model system for understanding the molecular mechanisms of development. By the onset of morphogenesis, ~6000 cells express distinct gene combinations according to embryonic position. Despite extensive mRNA in situ screens, combinatorial gene expression within individual cells is largely unknown. Therefore, it is difficult to comprehensively identify the coding and non-coding transcripts that drive patterning and to decipher the molecular basis of cellular identity. Here, we single-cell sequence precisely staged embryos, measuring >3100 genes per cell. We produce a transcriptomic blueprint of development - a virtual embryo where 3D locations of sequenced cells are confidently identified. Our \"Drosophila-Virtual-Expression-eXplorer\" performs virtual in situ hybridizations and computes expression gradients. Using DVEX, we predict spatial expression and discover patterned lncRNAs. DEVX is sensitive enough to detect subtle evolutionary changes in expression patterns between Drosophila species. We believe DVEX is a prototype for powerful single cell studies in complex tissues.

genomics↗

bioRxiv2017

Cell fixation and preservation for droplet-based single-cell transcriptomics

BackgroundRecent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells, in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not compromised by stress or ageing. Another challenge are rare cells that need to be collected over several days, or samples prepared at different times or locations.\n\nResultsHere, we used chemical fixation to overcome these problems. Methanol fixation allowed us to stabilize and preserve dissociated cells for weeks. By using mixtures of fixed human and mouse cells, we showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary single cells from dissociated complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells sorted by FACS, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide dropbead, an R package for exploratory data analysis, visualization and filtering of Drop-seq data.\n\nConclusionsWe expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single cell resolution.

genomics↗