Biology subjects

Kim, D. M.

Publications and source records attributed to Kim, D. M..

6 recordsLinked to original sources

bioRxiv2026

Toward Computationally Complete Spatial Omics

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.

genomics↗

bioRxiv2026

Molecular determinants of antibody-mediated priming to enhance detection of ctDNA

Liquid biopsies can enable cancer detection and monitoring yet remain limited by low concentrations of ctDNA. To address this limitation, we previously introduced a monoclonal antibody (mAb) "priming agent" that transiently increased the concentration of ctDNA in blood. Here, we investigated the molecular features that drive this effect. In a panel of novel mAbs that bound cfDNA, both those targeting dsDNA and those targeting mononucleosomes increased the concentration of ctDNA. mAbs with high avidity to dsDNA performed best, suggesting dsDNA as the key binding target. One agent preserved short, biologically informative molecules of cfDNA typically depleted at baseline. The Fc domain was dispensable, as F(ab)2 fragments retained priming activity. Leveraging these insights, we engineered single-chain agents using the dsDNA binding domain sso7d, expanding priming strategies beyond immunoglobulins. This study identifies the molecular features and general design principles for mAb-based priming agents to enhance recovery and detection of ctDNA. SignificanceLow concentration of ctDNA limits the sensitivity of liquid biopsies in many applications. This study shows how engineered antibodies and other dsDNA-binding molecules can inhibit clearance of ctDNA from the bloodstream and increase concentration of ctDNA in a blood draw, and identifies the key features that enable this activity. Modulation of cfDNA in the bloodstream using these agents can increase recovery of ctDNA and improve the performance of liquid biopsies for cancer detection.

bioengineering↗

bioRxiv2025

NRF2-Dependent Anti-Inflammatory Activity of Indole via Cell Surface Receptor Signaling in Murine Macrophages

In this study, we report indoles anti-inflammatory effects to be AhR-independent in RAW 264.7 macrophages. To explore the possibility of indoles surface-receptor mediated signaling, we developed an indole-bovine serum albumin conjugate (I3B), which primarily engage cell surface receptors and has limited intracellular engagement. Treatment with 10 M of I3B led to a comparable reduction of TNF- production in LPS-stimulated RAW 264.7 macrophages to that observed with 500 M of free indole. Transcriptome profiling of I3B-treated LPS-stimulated RAW 264.7 macrophages revealed, I3B blunts pro-inflammatory response and induces gene signatures consistent with NRF2 activation. LPS-stimulated NRF2-/- Bone Marrow-derived Macrophages (BMM) treated with I3B, showed higher levels of pro-inflammatory cytokine production relative to non-treated BMM. To define the upstream pathways responsible for this NRF2-depedent response, we examined GPCR-mediated signaling and found that I3B engages a Gq-coupled receptor to induce PKC{delta} phosphorylation, and subsequent NRF2 phosphorylation. Our results suggest I3B signals through a surface-receptor in a NRF2-dependent manner to reduce inflammation in murine macrophages. TeaserA cell-impermeant indole conjugate inhibits inflammatory signaling in macrophages through an NRF2-dependent mechanism.

cell biology↗

bioRxiv2024

Engineering Multiplexed Synthetic Breath Biomarkers as Diagnostic Probes

Breath biopsy is emerging as a rapid and non-invasive diagnostic tool that links exhaled chemical signatures with specific medical conditions. Despite its potential, clinical translation remains limited by the challenge of reliably detecting endogenous, disease-specific biomarkers in breath. Synthetic biomarkers represent an emerging paradigm for precision diagnostics such that they amplify activity-based biochemical signals associated with disease fingerprints. However, their adaptation to breath biopsy has been constrained by the limited availability of orthogonal volatile reporters that are detectable in exhaled breath. Here, we engineer multiplexed breath biomarkers that couple aberrant protease activities to exogenous volatile reporters. We designed novel intramolecular reactions that leverage protease-mediated aminolysis, enabling the sensing of a broad spectrum of proteases, and that each release a unique reporter in breath. This approach was validated in a mouse model of influenza to establish baseline sensitivity and specificity in a controlled inflammatory setting and subsequently applied to diagnose lung cancer using an autochthonous Alk-mutant model. We show that combining multiplexed reporter signals with machine learning algorithms enables tumor progression tracking, treatment response monitoring, and detection of relapse after 30 minutes. Our multiplexed breath biopsy platform highlights a promising avenue for rapid, point-of-care diagnostics across diverse disease states.

bioengineering↗

bioRxiv2024

Same-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment

The advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms. This iterative approach first entails single-cell spatial proteomics and rapid analysis to guide subsequent spatial transcriptomics capture on the same slide without loss in RNA signal. To enable multi-modal insights not possible with current approaches, we introduce k-bandlimited Spectral Graph Cross-Correlation (SGCC) for integrative spatial multi-omics analysis. Application of IN-DEPTH and SGCC on lymphoid tissues demonstrated precise single-cell phenotyping and cell-type specific transcriptome capture, and accurately resolved the local and global transcriptome changes associated with the cellular organization of germinal centers. We then implemented IN-DEPTH and SGCC to dissect the tumor microenvironment (TME) of Epstein-Barr Virus (EBV)-positive and EBV-negative diffuse large B-cell lymphoma (DLBCL). Our results identified a key tumor-macrophage-CD4 T-cell immunomodulatory axis differently regulated between EBV-positive and EBV-negative DLBCL, and its central role in coordinating immune dysfunction and suppression. IN-DEPTH enables scalable, resource-efficient, and comprehensive spatial multi-omics dissection of tissues to advance clinically relevant discoveries.

systems biology↗

bioRxiv2024

Microscale Spatial Dysbiosis in Oral biofilms Associated with Disease

Microbiome dysbiosis has largely been defined using compositional analysis of metagenomic sequencing data; however, differences in the spatial arrangement of bacteria between healthy and diseased microbiomes remain largely unexplored. In this study, we measured the spatial arrangement of bacteria in dental implant biofilms from patients with healthy implants, peri-implant mucositis, or peri-implantitis, an oral microbiome-associated inflammatory disease. We discovered that peri-implant biofilms from patients with mild forms of the disease were characterized by large single-genus patches of bacteria, while biofilms from healthy sites were more complex, mixed structures. Based on these findings, we propose a model of peri-implant dysbiosis where changes in biofilm spatial architecture allow the colonization of new community members. This model indicates that spatial structure could be used as a potential biomarker for community stability and has implications in diagnosis and treatment of peri-implant diseases. These results enhance our understanding of peri-implant disease pathogenesis and may be broadly relevant for spatially structured microbiomes.

microbiology↗