Biology subjects

Ramirez Flores, R. O.

Publications and source records attributed to Ramirez Flores, R. O..

18 recordsLinked to original sources

bioRxiv2026

The Human Gut Cell Atlas v1.0: A Unified, Community-BuiltReference of Cellular Diversity Across the Gut

Single-cell RNA-sequencing (scRNA-seq) studies have transformed our understanding of cellular diversity in the human intestine, but differences in anatomical sampling, experimental processing and cell-type nomenclature limit comparison across datasets. Here, we present the Human Gut Cell Atlas (HGCA) V1, a community-curated reference of 944,390 healthy and disease-adjacent intestinal cells from 502 samples, 265 donors and 24 landmark studies, linked to detailed anatomical, clinical, and technical metadata. As this metadata is of critical importance to human biology, we developed MetaManager, an open source data management framework for standardized metadata collection and tracking. A five-level consensus taxonomy resolves 94 homeostatic cell identities and enables transferrable, high-resolution annotation of new gut datasets. By assembling cell identity, sampling metadata, and independent spatial validation, intestinal segment and radial tissue depth were revealed to systematically shape cellular composition and transcriptional state, enabling inference of the presence of sampling-dependent tissue niches, including lymphoid follicles. Reference mapping accurately and rapidly recovered cohort-level Crohn's disease and treatment signatures without de novo annotation, while mapping human intestinal organoids quantified cell-type-specific identity, regional specification, maturation, and transcriptional fidelity to native tissue. The HGCA V1 provides a standardized cellular, anatomical coordinate, and metadata system for reproducible, high-quality analysis of intestinal biology across healthy tissue, disease-associated remodelling, and experimental models, providing a continuously evolving community reference for cumulative discovery in human intestinal biology.

systems biology↗

bioRxiv2026

mc-ASTRA maps population-level variation to interpretable multicellular tissue organization processes

Single-cell and spatial technologies increasingly enable detailed molecular profiling of tissues across biological contexts, creating opportunities to relate variation across samples to the multicellular processes that give rise to differences in tissue organisation and function. However, existing tools typically treat these aspects separately, either representing variation across samples or interpreting specific molecular and spatial features. To bridge this gap, we developed multicellular Analysis of Sample Tissue Representations and Associations (mc-ASTRA; https://github.com/saezlab/mc-astra), an open-source Python package for constructing maps of tissue-state variability that combine diverse tissue descriptors, including molecular, compositional and spatial features, together with complementary sample-level information such as clinical measurements or technical covariates, and biological prior knowledge of processes of interest. By combining available flexible semi-supervised factor models with established tools for the inference of biological processes from omics data, mc-ASTRA makes these tissue-state maps interpretable by quantifying how different tissue descriptors contribute to variation across samples and by resolving the specific coordinated multicellular programs underlying these differences. We illustrate the capabilities of mc-ASTRA across multiple single-cell and spatial omics datasets to infer multicellular programs that are contextualized by technical and biological prior knowledge and to describe distinct trajectories of tissue remodeling. mc-ASTRA provides a framework for building interpretable sample maps that connect tissue organization and multicellular mechanisms with variation observed across samples.

systems biology↗

bioRxiv2026

A transcriptional patient map of systemic lupus erythematosus reveals disease-related multicellular immune programs conserved between blood and kidney

Systemic lupus erythematosus (SLE) shows marked clinical and molecular heterogeneity, yet patient stratification often relies on gene expression signatures lacking multicellular context. Here we construct a transcriptional patient map of SLE by analyzing 1,167 total samples (783 SLE, 384 healthy controls) across different resolutions, including single-cell and bulk blood as well as spatially resolved kidney tissue transcriptomes. Using an unsupervised approach we inferred patient-level transcriptomic immune programs from two independent single-cell RNA sequencing cohorts of peripheral blood mononuclear cells (PBMCs), capturing both differences between SLE and health as well as within-SLE heterogeneity. Specifically, we identified four conserved programs comprising two multicellular inflammatory programs driven by interferon and TNF/NFkB activity across immune cells, and two cell type-specific programs reflecting CD8 T cell cytotoxicity and a CD4 T cell naive-to-effector state. Functional analysis of these programs revealed a rewiring of both cell-to-cell interactions and task allocation across cell types during disease activation. In addition, mapping these programs onto an external longitudinal blood transcriptomic cohort predicted flare risk and identified candidate blood protein biomarkers detectable by proteomics. Finally, we showed that these blood programs were enriched in immune-infiltrated glomerular regions from kidney biopsies of individuals with lupus nephritis using spatially resolved transcriptomic data, thereby linking systemic immune programs to local tissue pathology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/721379v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1e75f00org.highwire.dtl.DTLVardef@10e0a63org.highwire.dtl.DTLVardef@cc194forg.highwire.dtl.DTLVardef@191b4d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2026

Shared and organ-specific gene expression programs of fibrotic diseases

Fibrotic scarring is a common response to tissue injury. Repeated or severe insults can cause fibrosis, leading to excessive extracellular matrix deposition and a substantial clinical risk of organ dysfunction. Despite its high prevalence, few therapeutic options exist, and fibrotic diseases collectively represent a major global health burden. Fibrotic diseases affect virtually all organs, yet they have been explored mainly in isolation for each organ. As a result, proposed shared fibrotic mechanisms are often based on indirect comparisons between independent datasets rather than on a unified, systematic, cross-organ meta-analysis. To overcome this gap, we conducted a large-scale meta-analysis of single-cell transcriptomic data from healthy and fibrotic human tissues to identify both shared and organ-specific transcriptomic profiles. We constructed a single-cell fibrosis atlas of over five million cells from 20 studies, covering more than 25 disease etiologies affecting the heart, liver, kidney, and lung. Through systematic comparison of these datasets, we identified organ-specific as well as cross-organ fibrosis-associated gene expression profiles in major cell types and defined disease fibroblast subpopulations with excessive extracellular matrix production. These analyses revealed a conserved fibrotic response shared across tissues. Our analysis spans global comparisons of fibrosis-associated changes in cellular composition and predictive disease signatures to detailed examinations of individual genes, transcription factors, and intercellular communication patterns observed in fibrotic diseases across organs. We provide our cross-organ integration as a user-friendly open resource for investigating fibrotic diseases across organs. This resource will enable an accelerated discovery of disease mechanisms and faster development of broadly effective antifibrotic strategies in the future. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/709232v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@37e26borg.highwire.dtl.DTLVardef@f1ef53org.highwire.dtl.DTLVardef@1974d4corg.highwire.dtl.DTLVardef@53f5ea_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

genomics↗

bioRxiv2026

Modelling multicellular coordination by bridging cell-cell communication and intracellular regulation through multilayer networks

In multicellular organisms, cells with various roles and locations coordinate to provide systemic and cohesive response to perturbations. These complex behaviors emerge from a complex interplay between intracellular regulation and intercellular signals that mediate cell-cell communication. While single-cell technologies opened the possibility of studying both, most methods focus solely on one of these aspects. Thus, they are only able to partially recover in vivo and multicellular behaviors. We here introduce ReCoN (REconstruction of multicellular COordination Networks from single-cell data), a framework combining intracellular gene regulation and cell-cell communication to provide insights into multicellular coordination from single-cell data. First, ReCoN infers from single-cell data a heterogeneous multilayer network containing both cell-type-specific intracellular subnetworks and ligand-receptor interactions. Through random walk with restart explorations, ReCoN then infers the response of each cell type to both intra- and extracellular perturbations, such as a gene knock-out or a cytokine, respectively. ReCoN was evaluated on predicting the in vivo response of immune cell-types to different cytokines and on recovering cardiac cell-type response in heart failure. It highlighted the role of indirect effects, where cells emit secondary messengers in response to the initial perturbation to coordinate multicellular transcriptomic responses. Additionally, ReCoN predicted distinct fibroblast states emerging in different microenvironments reconstructed from spatial data. ReCoN provides an interpretable modeling framework for multicellular systems that allows for the simulation of perturbations, including the assessment of the cellular selectivity of these treatments in vivo. Ultimately, it can help design patient-specific molecular therapies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/700561v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@7cdceforg.highwire.dtl.DTLVardef@18cfa3eorg.highwire.dtl.DTLVardef@812645org.highwire.dtl.DTLVardef@f7bce5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

ParTIpy: A Scalable Framework for Archetypal Analysis and Pareto Task Inference

MotivationTrade-offs between different functions or tasks are pervasive across scales in biological systems. For example, individual cells cannot perform all possible functions simultaneously; instead they allocate limited resources to specialize in subsets of tasks by activating specific gene expression programs. Pareto Task Inference (ParTI) is a framework for analyzing biological trade-offs grounded in the theory of multi-objective optimality. However, existing software implementations of ParTI lack scalability to large datasets and do not integrate well with standard biological data analysis workflows, especially in the context of single-cell transcriptomics, limiting broader adoption. ResultsWe have developed ParTIpy (Pareto Task Inference in Python), an open-source Python package that combines advances in optimization and coreset methods to scale archetypal analysis, the primary algorithm underlying ParTI, to large-scale datasets. By providing additional tools to characterize archetypes, comprehensive documentation, and adopting standard scverse data structures, ParTIpy facilitates seamless integration into existing analysis workflows and broadens accessibility, particularly within the single-cell community. We demonstrate how ParTIpy can be used to study intra-cell-type gene expression variability through the lens of task allocation, offering a principled alternative to methods that impose discrete cell state classifications on inherently continuous variation. Availability and implementationParTIpys open-source code is available on GitHub (https://github.com/saezlab/ParTIpy) and pypi (https://pypi.org/project/partipy). Documentation is available at https://partipy.readthedocs.io. The code to reproduce the results of this paper is on GitHub (https://github.com/saezlab/ParTIpy_paper)

bioinformatics↗

bioRxiv2024

Comparison and evaluation of methods to infer gene regulatory networks from multimodal single-cell data

Cells regulate their functions through gene expression, driven by a complex interplay of transcription factors and other regulatory mechanisms that together can be modeled as gene regulatory networks (GRNs). The emergence of single-cell multi-omics technologies has driven the development of several methods that integrate transcriptomics and chromatin accessibility data to infer GRNs. While these methods provide examples of their utility in discovering new regulatory interactions, a comprehensive benchmark evaluating their mechanistic and predictive properties as well as their ability to recover known interactions is lacking. To address this, we built a comprehensive framework, Gene Regulatory nETwork Analysis (GRETA), available as a Snakemake pipeline, that includes state of the art methods decomposing their different steps in a modular manner. With it, we found that the GRNs were highly sensitive to methods choices, such as changes in random seeds, or replacing steps in the inference pipelines, as well as whether they use paired or unpaired multimodal data. Although the obtained networks performed well in predictive evaluation tasks and partially recovered known interactions, they struggled to capture causal relationships from perturbation assays. Our work brings attention to the challenges of inferring GRNs from single-cell omics, offers guidelines, and presents a flexible framework for developing and testing new approaches. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/629764v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@11a452forg.highwire.dtl.DTLVardef@1b44cb9org.highwire.dtl.DTLVardef@190dbdorg.highwire.dtl.DTLVardef@d4f66b_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling

Heart failure (HF) is characterized by severely reduced cardiac function and tissue remodeling, driven by complex multicellular regulatory processes. Extensive studies have generated molecular profiles at both bulk and single-cell levels; however, systematic integration that describes tissue-wide changes as a function of cell type coordination remains challenging. This disconnect hampers our understanding of the complex multicellular interactions driving heart failure, limiting our ability to translate molecular insights into actionable therapeutic strategies. Here, we integrated bulk and single-cell transcriptional profiles from cardiac tissues of HF and control patients across 25 studies, covering 1,524 individuals and seven cell-types, to delineate consensus multicellular transcriptional changes associated with cardiac remodeling. Our analyses revealed conserved cellular coordination events involving fibrotic, metabolic, inflammatory, and hypertrophic mechanisms, with fibroblasts playing a central role in predicting cardiomyocyte stress. Further analysis of fibroblast populations suggested that their activation in HF represents a broad phenotypic shift rather than solely accumulating distinct cell states. The integration of bulk and single-cell data within our data collection indicated that transcriptional responses to HF across cell types occur independently of tissue composition. Mapping independent data into our consensus programs demonstrated that recovery after left ventricular assist device implantation aligns with molecular recovery, highlighting the clinical relevance of the multicellular molecular state. Overall, our work synthesizes independent cardiac transcriptomics studies and makes the conserved HF associated insights available, establishing a reference for detailed exploration of HF-related multicellular molecular events. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/621815v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1b6fa5borg.highwire.dtl.DTLVardef@1ee9853org.highwire.dtl.DTLVardef@152a259org.highwire.dtl.DTLVardef@209640_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

systems biology↗

bioRxiv2023

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Transcriptomics, and in particular RNA-Seq, has become a widely used approach to assess the molecular state of biological systems. To facilitate its analysis, many tools have been developed for different steps, such as filtering lowly expressed genes, normalisation, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis using enrichment of gene sets, such as pathways, which typically provides the basis for interpretation and follow-up experiments. To address this gap, we introduce FLOP (FunctionaL Omics Processing), a comprehensive nextflow-based workflow that combines various methods for preprocessing and downstream enrichment analysis, allowing users to perform end-to-end analyses of count level transcriptomic data. We illustrate FLOP capabilities on diverse datasets comprising samples from end-stage heart failure patients and cancer cell lines in both basal and drug-perturbed states. We found that the correlation between gene set enrichment analysis results can vary significantly for alternative pipelines. Additionally, we observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space, especially in settings with limited statistical power. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/557538v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1b35ee7org.highwire.dtl.DTLVardef@11bd02aorg.highwire.dtl.DTLVardef@ff7b18org.highwire.dtl.DTLVardef@ea8d2c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

LIANA+: an all-in-one cell-cell communication framework

The growing availability of single-cell and spatially-resolved transcriptomics has led to the rapidly growing popularity of methods to infer cell-cell communication. Many approaches have emerged, each capturing only a partial view of the complex landscape of cell-cell communication. Here, we present LIANA+, a scalable framework to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially-resolved data. Beyond integrating and extending established methodologies and a rich knowledge base, LIANA+ enables novel analyses using diverse molecular mediators, including those measured in multi-omics data. Accessible as an open-source Python package at https://github.com/saezlab/liana-py, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/553863v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@16486ceorg.highwire.dtl.DTLVardef@ce6821org.highwire.dtl.DTLVardef@1165e54org.highwire.dtl.DTLVardef@18add5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1,183 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by investigating hallmarks of TF activity profiles inferred from the transcriptomes of three different cancer types. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/534849v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@ad6521org.highwire.dtl.DTLVardef@1ca8f37org.highwire.dtl.DTLVardef@1808ccdorg.highwire.dtl.DTLVardef@9c2613_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

Single-cell atlases across conditions are essential in the characterization of human disease. In these complex experimental designs, patient samples are profiled across distinct cell-types and clinical conditions to describe disease processes at the cellular level. However, most of the current analysis tools are limited to pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes and the effects of other biological and technical factors in the variation of gene expression. Here we propose a computational framework for an unsupervised analysis of samples from cross-condition single-cell atlases and for the identification of multicellular programs associated with disease. Our strategy, that repurposes multi-omics factor analysis, incorporates the variation of patient samples across cell-types and enables the joint analysis of multiple patient cohorts, facilitating integration of atlases. We applied our analysis to a collection of acute and chronic human heart failure single-cell datasets and described multicellular processes of cardiac remodeling that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlas and allows for the integration of the measurements of patient cohorts across distinct data modalities, facilitating the generation of comprehensive tissue-centric understanding of disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/529642v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@72269org.highwire.dtl.DTLVardef@645808org.highwire.dtl.DTLVardef@1cf9d6dorg.highwire.dtl.DTLVardef@1686e65_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Spatial cell type mapping of multiple sclerosis lesions

Multiple sclerosis (MS) is a prototypic chronic-inflammatory disease of the central nervous system. After initial lesion formation during active demyelination, inflammation is gradually compartmentalized and restricted to specific tissue areas such as the lesion rim in chronic-active lesions. However, the cell type-specific and spatially restricted drivers of chronic tissue damage and lesion expansion are not well understood. Here, we investigated the properties of subcortical white matter lesions by creating a cell type-specific spatial map of gene expression across various inflammatory lesion stages in MS. An integrated analysis of single-nucleus and spatial transcriptomics data enabled us to uncover patterns of glial, immune and stromal cell subtype diversity, as well as to identify cell-cell communication and signaling signatures across lesion and non-lesion tissue areas in MS. Our results provide insights into the conversion of the tissue microenvironment from a homeostatic to a pathogenic or dysfunctional state underlying lesion progression in MS. We expect that this study will help identify spatially resolved cell type-specific biomarkers and therapeutic targets for future interventional trials in MS.

neuroscience↗

bioRxiv2021

decoupleR: Ensemble of computational methods to infer biological activities from omics data

SummaryMany methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across methods perform better than other methods at predicting perturbed regulators. Availability and ImplementationdecoupleR is open source available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html). The code to reproduce the results is in Github (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208). ContactJulio Saez-Rodriguez at pub.saez@uni-heidelberg.de.

bioinformatics↗

bioRxiv2021

The spatial transcriptomic landscape of the healing intestine following damage

The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics (ST) to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we used our resource to map transcriptomics modules associated with human diseases demonstrating that ST can be used to inform clinical practice. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.

systems biology↗

bioRxiv2021

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods. FindingsO_LIBuilt a framework to systematically combine 15 resources and 6 methods to estimate cell-cell communication from single-cell RNA data C_LIO_LICell-cell communication resources are often built from the same original databases and very few interactions are unique to a single resource. Yet overlap varies among resources and certain biological terms are unevenly represented C_LIO_LIDifferent methods and resources provided notably different results C_LIO_LIThe observed disagreement among the methods could have a considerable impact on the interpretation of results C_LI

bioinformatics↗

bioRxiv2020

Spatial multi-omic map of human myocardial infarction

Myocardial infarction is a leading cause of mortality. While advances in the acute treatment have been made, the late-stage mortality is still high, driven by an incomplete understanding of cardiac remodeling processes1,2. Here we used single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of different physiological zones and timepoints of human myocardial infarction and human control myocardium to generate an integrative high-resolution map of cardiac remodeling. This approach allowed us to increase spatial resolution of cell-type composition and provide spatially resolved insights into the cardiac transcriptome and epigenome with identification of distinct cellular zones of injury, repair and remodeling. We here identified and validated mechanisms of fibroblast to myofibroblast differentiation that drive cardiac fibrosis. Our study provides an integrative molecular map of human myocardial infarction and represents a reference to advance mechanistic and therapeutic studies of cardiac disease.

systems biology↗

bioRxiv2020

Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data

The advancement of technologies to measure highly multiplexed spatial data requires the development of scalable methods that can leverage the spatial information. We present MISTy, a flexible, scalable and explainable machine learning framework for extracting interactions from any spatial omics data. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects, such as those from direct neighbours versus those from distant cells. MISTy can be applied to different spatially resolved omics data with dozens to thousands of markers, without the need to perform cell-type annotation. We evaluate the performance of MISTy on an in silico dataset and demonstrate its applicability on three breast cancer datasets, two measured by imaging mass cytometry and one by Visium spatial transcriptomics. We show how we can estimate interactions coming from different spatial contexts that we can relate to tumor progression and clinical features. Our analysis also reveals that the estimated interactions in triple negative breast cancer are associated with clinical outcomes which could improve patient stratification. Finally, we demonstrate the flexibility of MISTy to integrate different kinds of views by modeling activities of pathways estimated from gene expression in a spatial context to analyse intercellular signaling.

systems biology↗