Biology subjects

Enninful, A.

Publications and source records attributed to Enninful, A..

14 recordsLinked to original sources

bioRxiv2026

Determining cellular phylogeny and migration at high resolution in spatial genomics

Cellular migration and tumor evolution are central to tissue homeostasis and cancer biology. Rapid advances in spatial genomics have raised the possibility of using somatic mutations to track these processes in situ in human tissues. However, frequent data dropouts in spatial genomics measurements make this challenging to implement. Here, we present StarTrak (SpaTial Analysis for lineage ReconsTRuction with somatic vAriant calling and tracKing), a framework for somatic variant calling and lineage reconstruction at single-pixel spatial resolution. StarTrak employs a neural network framework to integrate mutational evidence (from both nuclear and mitochondrial reads), spatial proximity, histological images, and transcriptomic states to identify spatially informative patterns of mutations. It performs robustly across tissue types and spatial-omics platforms (being particularly successful with spatial ATAC-seq). Applying StarTrak to tumor sections, we infer the migration of mutated tumor cells into adjacent non-malignant tissues and reconstruct distinct lineage trees associated with different spatial regions. Notably, we identify tumor regions with nearly identical transcriptomic states but distinct mutational profiles. Moreover, for skin sections, StarTrak identifies sunlight-associated mutations in non-malignant epidermis and evidence suggesting the migration of these mutant surface cells into deeper skin layers. Overall, by integrating mutation calling with spatial context, StarTrak enables the inference of spatially resolved cellular migration and cellular phylogeny.

bioinformatics↗

bioRxiv2026

Multiscale harmonization and semantic integration of biomedical data enable biological insights through immersive exploration

Single-cell atlassing efforts like the Human BioMolecular Atlas Program (HuBMAP) and the Cellular Senescence Network (SenNet) are producing multiscale datasets across the healthy, adult, human body, but this data is typically explored only on 2D screens with limited 3D affordances, even though understanding a cells location within a tissue, organ, and body requires reasoning across many orders of spatial magnitude. The Human Reference Atlas (HRA) provides standard terminologies and a Common Coordinate Framework (CCF) for harmonizing such data spatially and semantically. Building on the HRA Organ Gallery in virtual reality (VR) application, we present "HRA: Powers of Ten," which integrates, harmonizes, and visualizes biological data from these efforts immersively in VR using a Multiscale Elevator System that lets users descend, like riding an elevator through an inverted skyscraper, from a whole body view of 81 reference organs to datasets across 5 organs (lymph node, brain, large intestine, small intestine, liver), 5 assay types (CODEX, Visium, v-CyCIF, Xenium, SBF-SEM), and 4 spatial scales. Contributed by Data Providers, these VR scenes enable biological insights into senescence patterns, cellular neighborhoods, 3D tissue reconstruction, and subcellular liver architecture. A standard operating procedure supports adding further datasets. Freely available on the Meta Store to over 20 million headset owners, the application, data, and code are open-source.

bioinformatics↗

bioRxiv2026

Human Lymph Node Cellular Senescence Atlas Reveals Age-Dependent Alteration in Germinal Center B Cell Function and Niches

Immunosenescence, the age-associated decline in immune function, is a key feature of human aging. In human lymphoid organs, however, the specific immune cell populations that acquire senescence-associated phenotypes during aging and how they influence the surrounding tissue microenvironment remain poorly understood. A spatially resolved map of these senescence-associated immune states in human lymphoid tissues could help clarify their relationship with aging and their potential contributions to the progressive decline of immune function. Here, we integrated single-cell and spatial multi-omics to systematically characterize age-related senescence in human lymph nodes (LNs). Single-cell transcriptomics of lymphoid tissues from donors aged 18 to 100 years old identified 34 immune and stromal cell types and revealed age-associated upregulation of senescence signatures in specific populations. Spatial proteomic profiling of 99 LN sections from 51 donors (18-86 years) using high-plex immunofluorescence ([~]20 million cells) mapped senescence markers (p16, p21, HMGB1, -H2AX) at single-cell resolution, revealing diverse senescent-like cell types ("senotypes") and a stepwise shift from extrafollicular to germinal center (GC) localization with age. Notably, we observed focal clonal-like senescence in GC B cells in older donor LNs. Spatial transcriptomics, epigenomics, and metabolic imaging of selected samples further elucidate the multi-omics signatures and underlying mechanisms of functional impairment, metabolic remodeling, and distinct regulatory programs in senescent-like GC B cells. This study presents a comprehensive spatial atlas of senescence-associated immune states in human lymph nodes, revealing cell-type-specific and spatial heterogeneity that may contribute to immunosenescence and the decline of immune function during aging.

immunology↗

bioRxiv2026

SenNet Portal: Build, Optimization and Usage

Cellular senescence is a hallmark of aging and a driver of functional decline across tissues, yet its heterogeneity and context dependence have limited systematic study. The Common Funds Cellular Senescence Network (SenNet) Program addresses this challenge by generating multimodal, multi-tissue datasets that profile senescent cells across the human lifespan and complementary mouse models. The SenNet Data Portal (https://data.sennetconsortium.org) serves as the public gateway to these resources, providing open access to harmonized single-cell, spatial, imaging, transcriptomic, and proteomic data; senescence biomarker catalogs; and standardized protocols that can be used to comprehensively identify and characterize senescent cells in mouse and human tissue. As of April 2026, the portal hosts 2,041 publicly available human and mouse datasets across 15 organs using 6 general assay types. Experts from 13 Tissue Mapping Centers (TMCs) and 12 Technology Development and Application (TDAs) components contribute tissue data, analyze data, identify senescent biomarkers, and agree on panels for cross-tissue antibody harmonization. They also register human tissue data into the Human Reference Atlas (HRA) and develop user interfaces for the multiscale and multimodal exploration of this data. Built on a scalable hybrid cloud microservices architecture by the Consortium Organization and Data Coordinating Center (CODCC), the Portal enables data submission, management, integrated analysis, spatial context mapping, and harmonized access to cross-species data critical for aging research. This paper presents user needs, the Portals architecture, data processing workflows, and senescence-focused analytical tools; usage scenarios illustrating applications in biomarker discovery, quality benchmarking, hypothesis generation, spatial analysis, cost-efficient profiling, and cell distance distribution analysis; and utility and usage by the larger researcher community. Current limitations and planned extensions--including expanded spatial-omics releases and improved tools for senotype characterization--are discussed. SenNet protocols, code, and user interfaces are freely available on https://docs.sennetconsortium.org/apis.

bioinformatics↗

bioRxiv2025

SuperFocus enables whole-slide cell-level spatial omics from spot-based measurements and histology across modalities

Histopathology and molecular pathology are currently distinct diagnostic modalities for the most part, one revealing tissue morphology at cellular resolution and the other providing molecular measurements with limited or no spatial context. Projecting genome-scale molecular information onto histopathology images at single-cell resolution across whole tissue sections represents a long-sought goal for next-generation pathology. Here we present SuperFocus, a modality-agnostic computational platform that generates histopathology-integrated single-cell spatial multi-omics from spot-based spatial measurements acquired on the same or an adjacent section without requiring external reference data. SuperFocus combines constrained cascading imputation with feature-level and cell-level quality-control scores to reduce spurious predictions and quantify confidence. On a ground-truth spatial transcriptomics benchmark dataset, SuperFocus improves key accuracy metrics by 28-73% over existing methods. Across Patho-DBiT, spatial ATAC-RNA, spatial CITE-seq and Visium-MALDI-MSI (SMA) datasets, SuperFocus enables cell-resolved analyses of MALT lymphoma microenvironments, gene regulatory programs in human hippocampus, lipotoxic hepatocyte states in human MASH, and transcriptomic-metabolomic states linked to neurotransmission and neuroinflammation in Parkinsonian mouse brain. Overall, SuperFocus enables scalable whole-slide single-cell spatial multi-omics integrated with histopathology, bridging histology and genome-scale molecular profiling for next-generation molecular pathology.

bioinformatics↗

bioRxiv2025

Exploring endothelial cell environments across organs in spatially resolved omics data

Endothelial cells are ubiquitously present in the human body and line the luminal surface of blood and lymphatic vessels. The oxygen-dependence of cells impacts their proximity to blood vessels, and consequently, to endothelial cells depending on their functional properties and priorities. This paper presents cell-to-nearest-endothelial-cell distance distributions for various cell types using 399 spatially resolved omics datasets from 14 studies comprising 12 tissue types with a total of 47,349,496 cells. Additionally, we developed an open-source web-based interactive tool, Cell Distance Explorer, that allows researchers to interactively visualize cell graphs and linkages in 2D and 3D datasets. Finally, we present a hierarchical neighborhood analysis focused on the endothelial cell neighborhoods in small and large intestine datasets. This paper provides an open-access resource (datasets, tools, and analyses) to characterize and compare cell distances and cell neighborhoods in spatially resolved omics data.

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↗

bioRxiv2024

Spatially Resolved in vivo CRISPR Screen Sequencing via Perturb-DBiT

Perturb-seq enabled the profiling of transcriptional effects of genetic perturbations in single cells but lacks the ability to examine the impact on tissue environments. We present Perturb-DBiT for simultaneous co- sequencing of spatial transcriptome and guide RNAs (gRNAs) on the same tissue section for in vivo CRISPR screen with genome-scale gRNA libraries, offering a comprehensive understanding of how genetic modifications affect cellular behavior and tissue architecture. This platform supports a variety of delivery vectors, gRNA library sizes, and tissue preparations, along with two distinct gRNA capture methods, making it adaptable to a wide range of experimental setups. In applying Perturb-DBiT, we conducted un-biased knockouts of tens of genes or at genome-wide scale across three cancer models. We mapped all gRNAs in individual colonies and corresponding transcriptomes in a human cancer metastatic colonization model, revealing clonal dynamics and cooperation. We also examined the effect of genetic perturbation on the tumor immune microenvironment in an immune-competent syngeneic model, uncovering differential and synergistic perturbations in promoting immune infiltration or suppression in tumors. Perturb-DBiT allows for simultaneously evaluating the impact of each knockout on tumor initiation, development, metastasis, histopathology, and immune landscape. Ultimately, it not only broadens the scope of genetic inquiry, but also lays the groundwork for developing targeted therapeutic strategies.

genomics↗

bioRxiv2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

systems biology↗

bioRxiv2024

All-Optical Multimodal Mapping of Single Cell Type-Specific Metabolic Activities via REDCAT

Metabolism underlies cell growth, survival, and function, yet its activities vary widely across cell types and tissue environments. Spatially resolving these processes in situ at single-cell resolution is essential to advance our understanding of cellular function and tissue physiology in health and disease. However, existing approaches are limited by either destructive workflows, insufficient spatial resolution and biochemical specificity, or lack of direct linkage to cell identity. Here, we present Raman Enhanced Delineation of Cell Atlases in Tissues (REDCAT), a multimodal all-optical platform that integrates stimulated Raman scattering, autofluorescence redox imaging, second harmonic generation, and high-plex immunofluorescence to co-map metabolic activities and cell types within the same tissue section. REDCAT achieves subcellular resolution profiling of protein, lipid, redox, and nuclear acid metabolism, together with extracellular matrix composition, in both FFPE and fresh-frozen human tissues. Applied to normal lymph nodes, REDCAT delineated distinct redox and lipid remodeling programs across germinal center B-cell zones and immune subsets, highlighting cell-type-specific metabolic specialization. In lymphoma, it revealed profound metabolic reprogramming, including extensive lipid accumulation, nuclear metabolic heterogeneity, and a transitional metabolic state associated with transformation from chronic lymphocytic leukemia to diffuse large B-cell lymphoma, thereby illuminating tumor evolution in situ. In human liver, REDCAT resolved cell-type-specific lipid droplet diversity and zonation-dependent nuclear metabolic gradients, uncovering new principles of spatial metabolic organization. By directly linking cell identity with spatial metabolic states at single-cell or subcellular resolution, REDCAT establishes a broadly applicable framework for studying immune function, tumor progression, and tissue physiology, and offers a new path to deciphering the metabolic basis of health and disease.

cell biology↗

bioRxiv2024

Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues

Spatial transcriptomics has emerged as a powerful tool for dissecting spatial cellular heterogeneity but as of today is largely limited to gene expression analysis. Yet, the life of RNA molecules is multifaceted and dynamic, requiring spatial profiling of different RNA species throughout the life cycle to delve into the intricate RNA biology in complex tissues. Human disease-relevant tissues are commonly preserved as formalin-fixed and paraffin-embedded (FFPE) blocks, representing an important resource for human tissue specimens. The capability to spatially explore RNA biology in FFPE tissues holds transformative potential for human biology research and clinical histopathology. Here, we present Patho-DBiT combining in situ polyadenylation and deterministic barcoding for spatial full coverage transcriptome sequencing, tailored for probing the diverse landscape of RNA species even in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for five years. Furthermore, genome-wide single nucleotide RNA variants can be captured to distinguish different malignant clones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA-mRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis trajectory. High resolution Patho-DBiT at the cellular level reveals a spatial neighborhood and traces the spatiotemporal kinetics driving tumor progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to study human tissue biology and aid in clinical pathology evaluation.

systems biology↗

bioRxiv2022

Spatial-CITE-seq: spatially resolved high-plex protein and whole transcriptome co-mapping

We present spatial-CITE-seq for high-plex protein and whole transcriptome co-mapping, which was firstly demonstrated for profiling 189 proteins and transcriptome in multiple mouse tissue types. It was then applied to human tissues to measure 273 proteins and transcriptome that revealed spatially distinct germinal center reaction in tonsil and early immune activation in skin at the COVID-19 mRNA vaccine injection site. Spatial-CITE-seq may find a range of applications in biomedical research.

genomics↗

bioRxiv2021

Spatial Epigenome Sequencing at Tissue Scale and Cellular Level

Spatial biology is emerging as a new frontier of biomedical research in development and disease, but currently limited to transcriptome and a panel of proteins. Here we present spatial epigenome profiling for three histone modifications (H3K27me3, H3K4me3, H3K27ac) via next-generation sequencing by combining in-tissue CUT&Tag chemistry and microfluidic deterministic barcoding. Spatial chromatin states in mouse embryos or olfactory bulbs revealed tissue type-specific epigenetic regulations, in concordance with ENCODE reference data, but providing spatially resolved genome-wide profiles at tissue scale. Using fluorescence imaging to identify the tissue pixels (20m) each containing one nucleus allowed us to extract single-cell epigenomes in situ. Spatial chromatin state profiling in tissue may enable unprecedented opportunities to study epigenetic regulation, cell function and fate decision in normal physiology and pathogenesis.

genomics↗

bioRxiv2020

Spatial transcriptome sequencing of FFPE tissues at the cellular level

Formalin-fixed paraffin-embedded (FFPE) tissues are the most abundant archivable specimens in clinical tissue banks, but unfortunately incompatible with single-cell level whole transcriptome sequencing due to RNA degradation in storage and RNA damage in extraction. We developed an in-tissue barcoding approach namely DBiT-seq for spatially revolved whole transcriptome sequencing at cellular level, which required no tissue dissociation or RNA exaction, thus potentially more suited for FFPE samples. Herein, we demonstrated spatial transcriptome sequencing of embryonic and adult mouse FFPE tissue sections at cellular level (25m pixel size) with high coverage (>1,000 genes per pixel). Spatial transcriptome of an E10.5 mouse embryo identified all major anatomical features in the brain and abdominal region. Integration with singlecell RNA-seq data for cell type identification indicated that most tissue pixels were dominated by single-cell transcriptional phenotype. Spatial mapping of adult mouse aorta, atrium, and ventricle tissues identified the spatial distribution of a variety of cell types. Spatial transcriptome sequencing of FFPE samples at cellular level may provide enormous opportunities in a wide range of biomedical research. It may allow us to exploit the huge resource of clinical tissue specimens to study human disease mechanisms and discover tissue biomarkers or therapeutic targets.

genomics↗