Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “omics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17Linked to original sources
PubMed2026

Decoding Primary Open-Angle Glaucoma: A Multi-Omics Approach to Identify Druggable Effector Genes.

PURPOSE: Genomewide association studies (GWAS) have identified numerous primary open angle glaucoma (POAG) risk loci, yet most reside in non-coding regions with unclear function. Mapping these loci to effector genes can elucidate disease mechanisms, identify functionally conserved variants, improve cross-ancestry risk prediction by reducing population-specific noise, and uncover shared therapeutic targets. METHODS: Here, we integrate European POAG GWAS with six types of multi-omics molecular Quantitative Trait Locis (xQTLs) using multi-trait colocalization to identify candidate effector variants and evaluate their cross-population relevance using genetic risk score (GRS) analysis, and their therapeutic potential through drug target prioritization. RESULTS: We identified 25 POAG effector variants colocalized with at least one xQTLs. In non-European populations, effector variants showed stronger effect size correlations with Europeans than non-colocalized variants (Pearson r2 = African 0.85 vs. 0.71; East Asian 0.81 vs. 0.69; and Latin American 0.91 vs. 0.75). Effector variants also had smaller allele frequency variations across populations (average interquartile range [IQR] = 0.15 vs. 0.20). The genetic risk score based on effector variants performed comparably to the genome-wide significant single-nucleotide polymorphism (SNP)-based GRS in non-European populations. Drug prioritization identified zinc, copper, sunitinib, probucol, and astemizole as potential common therapeutic agents for POAG and its subtypes. CONCLUSIONS: Our findings offer deeper insight into the molecular mechanisms underlying glaucoma and effector variants for developing more robust GRS models and broadly effective therapeutic strategies for POAG.

Humans↗
PubMed2026

Unleashing innovative cross-organ fibrosis therapies by harnessing the omics revolution.

Fibrosis is a major cause of mortality and morbidity worldwide with limited therapeutic options. Our understanding of fibrosis has significantly improved and led to the identification of "core" fibrogenic mechanisms that fuel a self-sustaining vicious cycle following the initial insult. The fibrotic niche is the result of complex cellular and molecular interactions that need to be disrupted to achieve transformational therapies. In this Review, we describe the current understanding of fibrogenic mechanisms, the progress and limitations of omics approaches in the identification of novel fibrotic pathways, and advances in therapeutic modalities that all together have the potential to unleash innovative cross-organ antifibrotic therapies.

Humans↗
PubMed2026

Multi-omics causal inference of childhood asthma triggered by ambient particulate matter.

BACKGROUND: The causal impact of fine particulate matter (PM2.5), an established environmental risk factor, on childhood asthma and its biological mechanisms remain to be elucidated. The objective of the present study was to evaluate the causal association between PM2.5 and childhood asthma and to dissect the mediating role of plasma proteins through a multi-omics integrated Mendelian randomisation (MR) framework. METHODS: Two-sample MR was performed on large-scale genome-wide association data to estimate the causal effect of PM2.5 on childhood asthma. Genes commonly associated with PM2.5 and childhood asthma were screened by transcriptome-wide association study (TWAS) and subjected to enrichment analyses and MR. Mediator proteins were identified by two-step MR. Potential adverse effects were scanned by phenome-wide MR (Phe-MR). RESULTS: MR revealed a significant positive causal effect of PM2.5 on childhood asthma (OR=1.897, 95% CI: 1.063-3.388, p=0.030). TWAS highlighted 70 genes co-expressed in PM2.5 and childhood asthma that were enriched in inflammatory pathways such as lysosome- and leukocyte-mediated immunity. MEAF6 was validated as a protective gene and RNF40 as a risk gene for childhood asthma. Two-step MR identified FUT10 as a positive mediator mediating 19.3% of the causal effect, and CD200 and MANBA as negative mediator proteins. Phe-MR indicated the association of these genes and proteins with multiple other diseases, implying possible adverse effects from therapeutic intervention. CONCLUSION: Long-term PM2.5 exposure is causally linked to childhood asthma with MEAF6, RNF40, CD200, MANBA and FUT10 identified as key molecules. The study provides new evidence for the biological mechanisms linking PM2.5 to childhood asthma.

Journal Article↗
PubMed2026

Multi-omics unveils seasonal remodeling and metabolic crosstalk between testis and abdominal fat body in a non-amplexus stream frog Nanorana taihangnica (Anura: Dicroglossidae).

BACKGROUND: Energy allocation between reproduction and survival represents a fundamental life-history challenge for animals in seasonal environments. Using integrated transcriptomics and metabolomics, we investigated Nanorana taihangnica (Anura: Dicroglossidae), a non-amplexus stream frog endemic to China, to elucidate the seasonal morphological and molecular coordination between the testis and abdominal fat body. RESULTS: Morphological analysis showed that fat body adipocyte cross-sectional area minimized at the end of the breeding season but rapidly recovered thereafter, while testicular volume continued declining post-breeding and only recovered during the non-breeding period. During breeding season, multi-omics analyses revealed that the fat body enhanced fatty acid oxidation, upregulated histidine-carnosine metabolism, activated NAD+ metabolism and FOXO3-mediated antioxidative responses to mitigate metabolic stress, and regulated adipocyte survival and apoptosis via sphingolipid signaling. Seasonal testicular development was centrally regulated by the mTOR signaling pathway, whose activity integrated autophagy levels, NAD+ availability, and aspartate metabolism to coordinate spermatogonial proliferation and spermatogenesis. CONCLUSIONS: This study demonstrates that N. taihangnica optimizes seasonal energy storage, allocation, and reproductive investment through molecular and metabolic crosstalk between the fat body and testis, providing empirical insights into the physiological integration of life-history strategies in animals inhabiting fluctuating environments.

Animals↗
PubMed2026

Integrated multi-omics analysis of fluoroquinolone tolerance mechanisms induced by enrofloxacin in Pasteurella multocida.

BACKGROUND: The global prevalence of multidrug-resistant bacteria has been rising at an alarming rate, posing a serious threat to both human and animal health. However, the mechanisms by which bacteria acquire antibiotic tolerance and subsequently develop resistance remain incompletely understood. METHODS: In this study, Pasteurella multocida, a common pathogen in the animal husbandry industry, was exposed to enrofloxacin, and genome resequencing, transcriptomic, and metabolomic analyses were performed to elucidate the adaptive mechanisms of P. multocida under fluoroquinolone-induced stress. RESULTS: Compared with the wild-type strain, the enrofloxacin-tolerant strain exhibited an extended lag phase, a prolonged logarithmic phase, reduced sensitivity to polymyxin B, reduced biofilm formation, and an elongated cellular morphology. Multi-omics analysis revealed a deletion in the dusB gene of the tolerant strain, resulting in a truncated non-functional protein. The deletion of dusB enhanced tolerance by prolonging the lag phase and reducing the growth rate. Moreover, the expression of genes in the CAMP pathway was up-regulated, and deletion of cpxR further promoted tolerance by modulating ribosome-associated genes. Integrated transcriptomic and metabolomic analyses indicated activation of the tricarboxylic acid (TCA) cycle during tolerance development. CONCLUSION: This study identified dusB and cpxR as key genes mediating enrofloxacin tolerance in P. multocida, elucidated the association between the antibiotic tolerance, growth, and gene expression, and may provide potential targets for future strategies aimed at limiting tolerance-associated resistance development.

Enrofloxacin↗
PubMed2025

CryoSCAPE: Scalable immune profiling using cryopreserved whole blood for multi-omic single cell and functional assays.

BACKGROUND: The field of single cell technologies has rapidly advanced our comprehension of the human immune system, offering unprecedented insights into cellular heterogeneity and immune function. While cryopreserved peripheral blood mononuclear cell (PBMC) samples enable deep characterization of immune cells, challenges in clinical isolation and preservation limit their application in underserved communities with limited access to research facilities. We present CryoSCAPE (Cryopreservation for Scalable Cellular And Proteomic Exploration), a scalable method for immune studies of human PBMC with multi-omic single cell assays using direct cryopreservation of whole blood. RESULTS: Comparative analyses of matched human PBMC from cryopreserved whole blood and density gradient isolation demonstrate the efficacy of this methodology in capturing cell proportions and molecular features. The method was then optimized and verified for high sample throughput using fixed single cell RNA sequencing and liquid handling automation with a single batch of 60 cryopreserved whole blood samples. Additionally, cryopreserved whole blood was demonstrated to be compatible with functional assays, enabling this sample preservation method for clinical research. CONCLUSIONS: The CryoSCAPE method, optimized for scalability and cost-effectiveness, allows for high-throughput single cell RNA sequencing and functional assays while minimizing sample handling challenges. Utilization of this method in the clinic has the potential to democratize access to single-cell assays and enhance our understanding of immune function across diverse populations.

Humans↗
PubMed2025

Integrative multi-omics analysis proposes a metabolic classification of gliomas: distinct metabolic states, immune infiltration, and prognosis.

BACKGROUND: The tumor microenvironment (TME) of glioma harbors diverse cell types; however, cell metabolic heterogeneity remains to be explored. This study aims to characterize the metabolic features of different cell types in the TME by integrating multiple datasets, including genomics, bulk and single-cell transcriptomics, and metabolomics. METHODS: Unsupervised machine learning was used to construct an energy metabolic classifier based on the metabolic pathways identified from bulk RNA-seq of gliomas in the TCGA dataset. The classifier was externally validated using multiple datasets, including genomics, bulk RNA-seq, snRNA-seq, and the metabolomics data. Furthermore, metabolic heterogeneity associated with the classifier was further characterized at single-cell resolution. RESULTS: The energy metabolism-based classifier stratified patients into two prognostic clusters: patients in cluster 1 were characterized by high pathway activity of glycolysis, the pentose phosphate pathway (PPP), and fatty acid oxidation (FAO), whereas patients in cluster 2 exhibited higher activity in glutaminolysis. This metabolic classifier revealed both intratumoral and intertumoral metabolic heterogeneity, and the complexity was further validated by the metabolomics profiling and snRNA-seq data from the CPTAC dataset. Notably, OSMR, highly expressed in cluster 1, showed significant co-expression with key glycolytic enzyme genes. The OSM/OSMR/JAK1/STAT3 axis potently drives malignant progression of glioma cells, specially enhancing their invasive and migratory capabilities. Single-cell resolution analyses demonstrated that tumor metabolic heterogeneity is primarily driven by malignant cells rather than non-malignant components, while tumor microenvironment (TME) factors were also found to modulate malignant cell metabolism. Significantly, glycolytic activity in glioma cells increased during the phenotypic transition from PN (proneural) to MES (mesenchymal), with cluster 1 metabolic phenotypes predominating in the tumor core. Compared to cluster 2, cluster 1 patients exhibited higher mRNA expression of immunosuppressive checkpoint genes, which correlated with pronounced immunosuppression in the TME. Furthermore, various immune cells demonstrated distinct metabolic preferences at single-cell resolution. CONCLUSIONS: This study developed an energy metabolic-based classifier for gliomas with prognostic and therapeutic potential. Metabolic reprogramming was linked with the PN-to-MES transition of glioma cells and immunosuppression in the tumor microenvironment. Multi-omics data, especially snRNA-seq, offered insights into metabolism heterogeneity at single-cell resolution, enabling personalized treatment strategies.

Humans↗
PubMed2026

Multi-omics identifies lipid accumulation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cell lines: a case-control study.

BACKGROUND: In recent years, evidence has indicated a metabolic shift towards increased demand for lipids in various lymphoid cell populations from people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). We previously screened the mitochondrial function and gene expression of B cell-derived lymphoblastoid cell lines (LCLs) generated from the blood of people with ME/CFS to characterise a model for hypothesis discovery and testing, observing elevated expression of gene products facilitating amino acid and fatty acid degradation for energy. METHOD: In this follow-up study we have expanded this characterisation by profiling the polar metabolomes and non-polar lipidomes of an all-female cohort of 17 healthy control and 15 ME/CFS LCLs, and we integrated this new data with the previously generated proteomic and transcriptomic data. RESULTS: In the polar metabolome we detected no significantly altered individual features, while integrated multi-omic analysis by MetaboAnalyst indicated 15 dysregulated pathways. Next, in the non-polar lipidome, we identified that PC(O-38:4) had significantly reduced levels in ME/CFS LCLs and was almost entirely discriminative of ME/CFS status. Among all detected classes of lipids we found that triradylglycerolipids ("triglycerides"), diradylglycerolipids and fatty acids were the most significantly affected and were elevated, and that most lipids exhibited average levels higher than in healthy controls. BioPAN pathway analysis of the lipidomic data predicted a more-active gene product that we confirmed to be significantly elevated in both our proteomic and transcriptomic data, this being phosphatidylserine synthase 1 (PTDSS1), plus 7 other gene products that were concordantly altered in expression in the transcriptomic data. We also found that ME/CFS LCLs exhibited a significant tendency towards more saturated lipid content. CONCLUSIONS: LCLs generated from circulating B cells from people with ME/CFS show accumulation of lipids, skewed lipid profiles and altered activity of related metabolic enzymes such as PTDSS1. These findings will inform future hypothesis-driven studies of primary lymphoid cell populations from people with ME/CFS to dissect specific immunometabolic mechanisms that may be involved in the syndrome, particularly relating to intersections between lipid abnormalities and potential effects on immune cell effector functions.

Fatigue Syndrome, Chronic↗
PubMed2026

Genome-resolved multi-omics provide new insights into microbial nitrogen utilization by the rumen microbiota.

BACKGROUND: Optimizing nitrogen (N) utilization in ruminant production systems holds both economic and environmental significance. However, traditional paradigms of N metabolism, derived primarily from well-studied model rumen bacteria, do not fully reflect the diverse and complex N metabolism in the rumen ecosystem. RESULTS: To address this gap, we utilized comparative genomics and genome-resolved multi-omics analyses using a curated set of microbial genomes to investigate N assimilation and regulation in rumen microbes. We discovered that well-established mechanisms of ammonia assimilation and regulation, such as the glutamine synthetase (GS)/glutamate synthase (GOGAT) pathways and their regulatory proteins, are absent in many of the predominant rumen microbes, which likely utilize alternative pathways for ammonia assimilation. These findings challenge the applicability of E. coli-based N regulation models to rumen bacteria in response to ammonia availability. We further linked polysaccharide utilization and ammonia assimilation across hundreds of rumen microbial species. Furthermore, we identified specific microbial species involved in ureolysis and denitrification, as well as phages carrying auxiliary metabolic genes involved in N assimilation. Using an animal trial involving 11 pairs of lamb twins in a crossover design, we demonstrated that dietary crude protein (CP) at 10% and 13% had minimal impact on rumen microbiome composition and expression of N assimilation genes. Instead, changes in concentrate levels altered N assimilation, notably increasing expression of amino acid biosynthesis pathways. CONCLUSION: These findings indicate a nuanced, species-specific microbial response to dietary interventions, highlighting the limitations of traditional N metabolism models applied to rumen microbes and the need for more granular studies of rumen microbial ecosystems.

Multiomics↗
PubMed2025

Multi-omics Mendelian randomization integrating RNA-seq, eQTL and pQTL data revealed CPXM1 as a potential drug target for osteoporosis.

Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mineral density and increased fracture risk, continues to be a major global health concern. Traditional treatments for osteoporosis have limited efficacy and safety profiles, highlighting the need for novel therapeutic targets. This study integrates multi-omics data, including RNA-seq, expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data, through Mendelian randomization (MR) to identify potential drug targets for osteoporosis. By leveraging bidirectional two-sample MR analysis, we identified CPXM1 (Carboxypeptidase X, M14 family member 1) as a novel gene that is causally linked to osteoporosis risk. Through transcriptomic and proteomic validation, we demonstrate that CPXM1 was upregulated in aged bone tissues and osteoporotic conditions in both human and murine models. Gene set enrichment analysis (GSEA) revealed significant dysregulation of bone homeostasis pathways, including increased extracellular matrix degradation and suppression of osteoblast differentiation in aged mice. Furthermore, phenome-wide association studies (PheWAS) confirmed minimal off-target effects of CPXM1, reinforcing its potential as a therapeutic target. Finally, computational drug repurposing predicted several promising drug candidates, including Doxorubicin, 5-Fluorouracil, and 2-Methylcholine, which may target CPXM1 pathways for osteoporosis treatment. These findings highlight CPXM1 as a potential biomarker and therapeutic target, offering new avenues for osteoporosis therapy.

Osteoporosis↗
PubMed2025

Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.

The study of ubiquitous circadian rhythms in human physiology requires regular measurements across time. Repeated sampling of the different internal tissues that house circadian clocks is both practically and ethically infeasible. Here, we present a novel unsupervised machine learning approach (COFE) that can use single high-throughput omics samples (without time labels) from individuals to reconstruct circadian rhythms across cohorts. COFE can simultaneously assign time labels to samples and identify rhythmic data features used for temporal reconstruction, while also detecting invalid orderings. With COFE, we discovered widespread de novo circadian gene expression rhythms in 11 different human adenocarcinomas using data from The Cancer Genome Atlas (TCGA) database. The arrangement of peak times of core clock gene expression was conserved across cancers and resembled a healthy functional clock except for the mistiming of a few key genes. Moreover, rhythms in the transcriptome were strongly associated with the cancer-relevant proteome. The rhythmic genes and proteins common to all cancers were involved in metabolism and the cell cycle. Although these rhythms were synchronized with the cell cycle in many cancers, they were uncoupled with clocks in healthy matched tissue. The targets of most of FDA-approved and potential anti-cancer drugs were rhythmic in tumor tissue with different amplitudes and peak times. These findings emphasize the utility of considering "time" in cancer therapy, and suggest a focus on clocks in healthy tissue rather than free-running clocks in cancer tissue. Our approach thus creates new opportunities to repurpose data without time labels to study circadian rhythms.

Humans↗
PubMed2025

Alterations in ether lipid metabolism in obesity revealed by systems genomics of multi-omics datasets.

Ratios between two metabolites are sensitive indicators of metabolic changes. Lipidomic profiling studies have revealed that plasma ether lipids, a class of glycero- and glycerophospho-lipids with reported health benefits, are negatively associated with obesity. Here, we utilized lipid ratios as surrogate markers of lipid metabolism to explore the processes underlying the inverse relationship between ether lipid metabolism and obesity. Plasma lipidomics data from two independent human cohorts (n = 10,339 and n = 4,492) were integrated to assess the associations between 82 lipid ratios and obesity-related markers in males and females. Results were externally validated using mouse transcriptomics data from the Hybrid Mouse Diversity Panel (n = 152-227 across 74 strains). Genome-wide association studies using imputed genotypes from a population cohort (n = 4,492) were performed to examine the genetic architecture of the ratios. Findings showed that waist circumference (WC), body mass index, and waist-hip ratio were inversely associated with total plasmalogens relative to total phospholipids in both sexes. Ratios comprising product-substrate pairs positioned either side of enzymes involved in plasmalogen synthesis and degradation showed positive and negative associations with WC, respectively. Branched-chain fatty acids negatively correlated with WC, while omega-6 polyunsaturated fatty acids exhibited differing associations depending on their position within the pathway. Mouse transcriptomics corroborated these results. Genomics data showed strong associations between ratios containing choline-plasmalogens and single-nucleotide polymorphisms in the transmembrane protein 229B (TMEM229B) gene region. This work demonstrates the utility of lipid ratios in understanding lipid metabolism. By applying the ratios to multi-omic datasets, we identified alterations in enzymatic activity and genetic variants likely affecting ether lipid synthesis in obesity that could not have been obtained from lipidomics data alone. Additionally, we characterized a potential role for TMEM229B, offering new perspectives on ether lipid metabolism and regulation.

Humans↗
PubMed2025

HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data.

High-dimensional mixed-effects models are an increasingly important form of regression in which the number of covariates rivals or exceeds the number of samples, which are collected in groups or clusters. The penalized likelihood approach to fitting these models relies on a coordinate descent algorithm that lacks guarantees of convergence to a global optimum. Here, we empirically study the behavior of this algorithm on simulated and real examples of three types of data that are common in modern biology: transcriptome, genome-wide association, and microbiome data. Our simulations provide new insights into the algorithm's behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently outperforms the least absolute shrinkage and selection operator (LASSO) penalty in terms of both variable selection and estimation accuracy across omics data. To empower researchers in biology and other fields to fit models with the SCAD penalty, we implement the algorithm in a Julia package, HighDimMixedModels.jl.

Algorithms↗
PubMed2025

OmnibusX: A unified platform for accessible multi-omics analysis.

OmnibusX is an integrated, privacy-centric platform that enables code-free multi-omics data analysis by bridging computational methodologies with user-friendly interfaces. Designed to overcome challenges posed by fragmented analytical tools and high computational barriers, OmnibusX consolidates workflows for diverse technologies - including bulk RNA-seq, single-cell RNA-seq, single-cell ATAC-seq, and spatial transcriptomics - into a single, cohesive application. The application integrates established open-source tools such as Scanpy, DESeq2, SciPy, and scikit-learn into transparent, reproducible pipelines, offering users control over analytical parameters. Additionally, OmnibusX features proprietary modules, including a highly accurate cell-type prediction engine and an interactive plotting editor for generating publication-quality visualizations. Available as a standalone desktop application and an enterprise edition for centralized server deployment, OmnibusX ensures all data processing is conducted locally, eliminating external data transfer and usage tracking. By lowering technical barriers and enhancing reproducibility, OmnibusX aims to accelerate biological discovery and foster robust, data-driven collaborations. A fully documented trial version is accessible at: https://omnibusx.com/apps.

Computational Biology↗
PubMed2024

Integrative omics analysis identifies biomarkers of septic cardiomyopathy.

Septic Cardiomyopathy (SCM) is a syndrome of acute cardiac dysfunction in septic patients, unrelated to cardiac ischemia. Multiomics studies including transcriptomics and proteomics have provided new insights into the mechanisms of SCM. In here, a rat model of SCM was established by intraperitoneal injection of lipopolysaccharide (LPS). Biomarkers of SCM were characterized via a multi-omics analysis. The differentially expressed (DE) mRNAs predominantly appeared in pathways linked to the immune response, inflammatory response, and the complement and coagulation cascades, while DE proteins were mainly enriched in pathways associated with the complement and coagulation cascades. On this basis, the integrated analysis was performed between transcriptome and proteome. The potential biomarkers were further verified by RT-qPCR and WB. The current proteotranscriptomic research has furnished a valuable dataset and fresh perspectives that will enhance our comprehension of the development of SCM. This, in turn, is expected to expedite the formulation of novel approaches for the prevention and management of SCM in patients.

Cardiomyopathies↗
PubMed2005

Joint EFPIA/CHMP SWP workshop: the Emerging Use of Omic Technologies for Regulatory Non-Clinical Safety Testing.

A workshop was held on October 26-27, 2004, in Bonn, Germany, to discuss the potential use of omic technologies for regulatory non-clinical safety testing of pharmaceuticals. The meeting was hosted by the European Federation of Pharmaceutical Industries and Associations (EFPIA). The workshop was held in conjunction with the 6th European preclinical assessors meeting, which was organized in Bonn by the German Federal Institute for Drugs and Medical Devices (BfArM) and the Safety Working Party (SWP) of the Committee for Medicinal Products for Human Use (CHMP). Approximately 100 scientists, roughly half from the European pharmaceutical industry and half from European regulatory authorities, attended the workshop. The authors of this report constitute the organizing committee members.

Biotechnology↗
PubMed2024

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.

Journal Article↗