Biology subjects

Bai, Z.

Publications and source records attributed to Bai, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Reprogramming CAR-T cell function through synthetic cytokine receptors suppresses metastatic progression of TNBC

Patient T cells modified with chimeric antigen receptors (CARs) have shown remarkable therapeutic success in previously incurable cancers. However, despite its success in hematological malignancies, CAR-T therapy has shown only modest efficacy in solid tumors. The medical field has focused on developing new molecular designs that enhance CAR-T persistence, prevent exhaustion in the tumor microenvironment (TME), and increase the specificity of the CAR-T cell to various tumor antigens. However, much less effort has been placed on enhancing CAR-T cells' invasion into and migration through the stromal matrix of the TME, where they can elicit tumor-directed cytotoxicity. Here, we engineered CAR-T cells with synthetic cytokine receptors targeting inflammatory cytokines produced by the cells themselves, which increase their speed through collagen I matrices. We show that this enhanced motility is driven by increased CAR-T cell translocation through confined microchannels via cellular deformation, together with elevated migratory speeds in proximity to neighboring cells, suggesting improved responsiveness to local cues. These migration-enhanced CAR-T cells significantly reduce both primary tumor size and distant organ metastasis in triple-negative breast cancer (TNBC) mouse models compared to standard CAR-T cells. These results support molecular design strategies that exploit mechanisms to improve cellular therapies for metastatic solid tumors.

bioengineering↗

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

Episodic memory rescues working memory via pattern separation, pattern completion, and predictive recall

Working memory is capacity-limited, but interactions with episodic memory may offset this constraint. We tested moment-by-moment contributions of episodic representations to working memory by combining the N-back and Mnemonic Similarity tasks. Thirty-one participants, undergoing eye-tracking, first encoded items in a one-back task, classifying them as "same" or "similar" to their predecessor. In a subsequent two-back task, mnemonic discrimination showed a graded, item-specific benefit of prior experience: performance was best for previously compared items, whereas recognition of identical repeats was unaffected. Successful discrimination of previously compared items was accompanied by greater pupil dilation, gradually emerging gaze patterns resembling those elicited by their similar pair-mate, and higher gaze-similarity between one-back and two-back target viewing. Diverging gaze patterns between pair-mates during one-back further predicted two-back discrimination. These findings challenge working memory's characterization as an isolated system, demonstrating how it recruits episodic computations - encoding distinct traces, predicting upcoming content, and reinstating it at retrieval.

neuroscience↗

bioRxiv2026

Single-cell foundation models predict durable CAR T response despite imperfect cell annotation

CD19-targeted chimeric antigen receptor (CAR) T cell therapy achieves high initial response rates in B-cell acute lymphoblastic leukemia (B-ALL), yet half of patients relapse within one year. Pre-infusion product composition decoded by single-cell RNA sequencing (scRNA-seq) carries information predictive of long-term CAR T persistence, but extracting this information from individual patients typically requires highly sophisticated bioinformatics expert annotation, limiting clinical translation. Here, we evaluate whether single-cell foundation models (scFMs) can extract clinically actionable information from engineered CAR T products. We applied four scFMs (scGPT, scFoundation, CellPLM and UCE), including fine-tuned versions of scGPT and scFoundation, to paired basal and CD19-stimulated pre-infusion CAR T products from 33 pediatric patients with B-ALL. Although annotation accuracy declined relative to healthy peripheral blood references, scFM-derived cell composition stratified patients with long-duration B-cell aplasia with a leave-one-out cross-validated area under the receiver operating characteristic curve of 0.879 (95% confidence interval, 0.742-0.986). Notably, foundation-model-identified cell proportion analysis matched or exceeded expert annotations for several predictive features, demonstrating that accurate clinical prediction may not require perfect per-cell annotation to begin with. CD8+XCL1/2+ cells were further identified as the biomarker consistently associated with durable CAR T persistence across models under CD19 stimulation, whereas other candidate populations showed limited reproducibility. Finally, we translate these findings into a locally deployable decision-support AI agent that predicts the probability of sustained CAR T persistence from pre-infusion CAR T scRNA-seq data.

systems biology↗

bioRxiv2026

Pan-cancer circular genomics identifies intratumoral Staphylococcus lugdunensis as a metabolic driver in bladder cancer

Intratumoral microbiota play key roles in cancer but are challenging to profile due to low biomass. We developed CIRCMIP, an enzymatic pipeline that eliminates linear DNA to enrich bacterial genomes, achieving 100-fold higher sensitivity than standard metagenomics. Applied to 312 pan-cancer specimens, CIRCMIP identified Staphylococcus lugdunensis as a signature bacterium enriched in early-stage bladder cancer (BLCA), where its presence predicts poor survival. Integrated modeling and lipidomics revealed that Staphylococcus lugdunensis colonization drives aberrant lipid metabolism with secretion of LPC14:0. Both Staphylococcus lugdunensis and LPC14:0 drive BLCA progression by promoting fatty acid uptake and {beta}-oxidation. Mechanistically, chemical proteomics revealed LPC14:0 as a direct PPAR{delta} ligand, binding via hydrogen bonds with Thr292/Thr289. This activation upregulates fatty acid transporters (CD36, FABP4) and metabolic enzymes (ACOX2), fueling malignant proliferation. Furthermore, CIRCMIP-derived biomarkers show robust diagnostic accuracy, establishing a new research paradigm and revealing the Staphylococcus lugdunensis-LPC14:0-PPAR{delta} axis as a therapeutic target in bladder cancer. HIGHLIGHTO_LICIRCMIP leverages DNA circularity to achieve 100-fold sensitivity in pan-cancer bacteriome profiling. C_LIO_LIStaphylococcus lugdunensis is a bacterial signature in early-stage bladder cancer that predicts poor patient survival. C_LIO_LIBacteria-derived LPC14:0 acts as a direct ligand for PPAR{delta} to drive host malignant metabolic reprogramming. C_LIO_LITargeting the Staphylococcus lugdunensis-LPC14:0-PPAR{delta} axis abrogates malignant progression in bladder cancer. C_LI

cancer biology↗

bioRxiv2026

Long-lived yeast-derived lipids promote longevity via regional and systemic metabolic remodeling

Aging is marked by a progressive breakdown of intestinal integrity and metabolic homeostasis, which together drives systemic decline in physiology and reduces lifespan. Here, we found that dietary lipids extracted from a genetically engineered long-lived yeast strain robustly extend lifespan in Drosophila and further uncovered the mechanisms using deuterium oxide-probed stimulated Raman scattering microscopy to image metabolic dynamics and single nucleus RNA sequencing (scRNA-seq) to unveil the underlying pathways. These yeast lipids are enriched in shorter, more saturated fatty acids and phospholipids as revealed by Raman spectroscopy and lipidomics, contributing to increased membrane order and reduced lipid storage. Functionally, targeted dietary supplementation with these lipid components synergistically prolongs fly lifespan. We show that these lipids reverse age-related declines in gut lipid droplet abundance, enhance membrane lipid incorporation, and increase de novo lipid synthesis, thereby improving epithelial structural integrity and barrier function. snRNA-seq identifies transcriptional remodeling in metabolically active enterocytes, including upregulation of autophagy and protein turnover genes, alongside reduction of unsaturated fatty acid biosynthesis. In the brain, dietary lipids orchestrate a dual metabolic strategy--promoting energy conservation and enhanced signaling across most neuronal and glial populations, while selectively boosting mitochondrial function in memory-critical Kenyon cells. All these leads to the enhancement of gut-to-glia communication, particularly through EGFR and FGFR pathways. Finally, analysis of our data with the Fly Metabolic Analysis Pipeline (FLY-MAP) reveals that yeast lipids restructure gut metabolic modules to coordinate energy production, redox balance, and nutrient flexibility. Our study uncovers a cross-kingdom mechanism of metabolic longevity regulation, paving the way for leveraging yeast-derived nutritional components to support tissue homeostasis and promote healthy aging.

molecular biology↗

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

Explainable Physicochemical Determinants of Protein Ligand Binding via Non-Covalent Interactions

Protein-ligand binding governs enzymatic catalysis, metabolic homeostasis, and therapeutic modulation. Thus, the accurate prediction of these interactions underpins modern rational drug discovery. However, existing deep-learning frameworks largely operate as black-box predictors that fail to resolve the individual residues mediating binding or decode the fundamental non-covalent forces that drive molecular recognition. To address these limitations, we present ExplainBind, an interaction-aware framework that predicts binding likelihood, localizes specific binding residues at single-amino-acid resolution rather than coarse pocket-level regions, and decodes the underlying non-covalent interaction patterns, all zero-shot, without requiring prior three-dimensional structural inputs. To support residue- and interaction-level training and evaluation, we construct InteractBind, a protein-ligand benchmark with residue-atom interaction maps. Simultaneously, benchmarking experiments demonstrate that ExplainBind consistently outperforms state-of-the-art baselines across diverse protein and ligand spaces, maintaining high precision when generalized to entirely novel sequences and chemical scaffolds. When applied to two unseen therapeutic targets, ExplainBind successfully ranks potent angiotensin-converting enzyme (ACE) inhibitors and clarifies differences in their potency via affinity-stratified interaction landscapes. Furthermore, we demonstrate the prospective utility of ExplainBind by discovering novel inhibitors and activators of L-2-hydroxyglutarate dehydrogenase (L2HGDH) through wet-lab validation, with mechanistically distinct interaction profiles providing a clear molecular rationale for their divergent functional outcomes. Collectively, these results establish ExplainBind as a powerful, generalizable tool for mechanistically informed, interpretable drug discovery.

molecular biology↗

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

A multi-tissue atlas of genetic regulatory effects in sheep

Characterizing the impact of genomic variants on genome function and ultimately complex traits in livestock is essential for the development of sustainable precision agriculture and comparative genomics. Here, as part of the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we present the pilot phase of the SheepGTEx resource through analyzing 6,761 RNA-sequencing samples of 51 primary tissues in a multi-breed population of sheep. We identify millions of regulatory variants associated with seven types of molecular phenotypes, and fine-map 322,468 primary and 114,017 non-primary effects, revealing a high degree of regulatory allelic heterogeneity. We systematically characterize the pleiotropic effects of these variants on molecular phenotypes, assess their context-specific regulatory patterns across tissues, breeds, sexes, and developmental stages, as well as explore their evolutionary constraints across mammals. Finally, we demonstrate the substantial potential of the SheepGTEx resource (https://sheepgtex.farmgtex.org), by providing examples of regulatory mechanisms underpinning 34 complex traits, population divergence between European and Asian breeds, and adaptive evolution in sheep over the past ten millennia.

genomics↗

bioRxiv2025

Stress drives the hippocampus to prioritize statistical prediction over episodic encoding

The hippocampus plays a critical role in encoding individual experiences into episodic memory, while also detecting patterns shared across these experiences that allow us to anticipate future events via statistical learning. Acute stress is known to impact the hippocampus but may have opposite effects on these competing functions. That is, although stress impairs episodic encoding, it may instead enhance statistical learning. Using functional magnetic resonance imaging, we tested how stress influences the hippocampal pathways and subfields that support episodic encoding and statistical learning while participants performed a task that engaged both processes. Across several analyses, stress biased hippocampal processing in favour of statistical learning, suppressing pattern separation between events in the dentate gyrus and enhancing the representation of statistically predictive features. Furthermore, stress drove the hippocampal monosynaptic pathway to support statistical learning rather than episodic encoding during predictive situations. Together, these data suggest that acute stress elicits targeted, adaptive changes in hippocampal pathways which may facilitate predicting and responding to upcoming events.

neuroscience↗

bioRxiv2025

Imaging Cellular Metabolic Rewiring with SuMMIT-SRS

Cells dynamically rewire their metabolic pathways in response to physiological and pathological cues. Such plasticity is particularly critical in neurons, stem cells, cancer cells, and immune cells, where biosynthetic demands can shift rapidly. However, current metabolic imaging techniques using isotope labeling typically track only one metabolite at a time, limiting their ability to capture the rapid dynamics of complex metabolic networks including coordinated precursor utilization, crosstalk, and turnover. Here, we present Subcellular Multiplexed Metabolic Isotope Tracing Stimulated Raman Scattering microscopy (SuMMIT-SRS), a platform that enables simultaneous visualization of multiple metabolic dynamics at subcellular resolution. By exploiting the distinct vibrational signatures of carbon-deuterium bonds derived from multiple deuterated amino acids, lipids, and monosaccharide tracers, SuMMIT-SRS maps co-regulated DNA, RNA, protein, and lipid synthesis at the same time and resolves various individual amino acid-mediated metabolic pathways within intact cells and tissues. We demonstrate SuMMITs broad utility across Drosophila fat body tissue and developing brain, tumor organoids, aged human neurons, and mouse liver, capturing cell type-specific metabolic rewiring under genetic and pathological perturbations. This approach extends SRS to multiplexed isotope tracing, offering a powerful tool to uncover dynamic and complex biosynthesis programs in development, health, and disease.

bioengineering↗

bioRxiv2025

Protection and deprotection of the Rec8 cohesin complex during meiosis

In the first meiotic division, the cohesin Rec8 is cleaved by separase along the chromosome arms but is protected at the centromere by shugoshin (Sgo1), maintaining centromeric cohesion throughout meiosis I. Another meiotic regulator, meikin (Moa1), supports the protective function of Sgo1 primarily by phosphorylating S450 of Rec8. Both Sgo1 and Moa1 are meiosis I-specific proteins. Unlike fission yeast Sgo1, mammalian shugoshin (SGO2) persists at centromeres throughout meiosis I and II, but REC8 cohesin is protected only during meiosis I. Here, we show that fission yeast Moa1 and Sgo1 appear at kinetochores/centromeres from prophase I to metaphase I and are degraded during anaphase I by the APC/C-Slp1 pathway. We expressed non-degradable versions of Moa1 and Sgo1 in meiosis II and investigated how these proteins protect the cohesin Rec8 during meiosis II. Our analyses suggest that the localization of Sgo1 and the phosphorylation of Rec8 at S449 and S450 are necessary and sufficient events for protecting Rec8 cohesin during meiosis II. The absence of either event leads to deprotection at meiosis II.

cell biology↗

bioRxiv2025

ASTRO: Automated Spatial Whole-Transcriptome RNA-Expression Workflow

MotivationDespite significant advances in spatial transcriptomics, the analysis of formalin-fixed paraffin-embedded (FFPE) tissues, which constitute most clinically available samples, remains challenging. Additionally, capturing both coding and noncoding RNAs in a spatial context poses significant challenges. We recently introduced Patho-DBiT, a technology designed to address these unmet needs. However, the marked differences between Patho-DBiT and existing spatial transcriptomics protocols necessitate specialized computational tools for comprehensive whole-transcriptome analysis in FFPE samples. ResultsHere, we present ASTRO, an automated pipeline developed to process spatial transcriptomics data. In addition to supporting standard datasets, ASTRO is optimized for whole-transcriptome analyses of FFPE samples, enabling the detection of various RNA species, including non-coding RNAs such as miRNAs. To compensate for the reduced RNA quality in FFPE tissues, ASTRO incorporates a specialized filtering step and optimizes spatial barcode calling, increasing the mapping rate. These optimizations allow ASTRO to spatially quantify coding and non-coding RNA species in the entire transcriptome and achieve robust performance in FFPE samples. AvailabilityCodes are available at GitHub (https://github.com/gersteinlab/ASTRO).

bioinformatics↗

bioRxiv2025

KIDINS220 and InsP8 safeguard the stepwise regulation of phosphate exporter XPR1

XPR1 is emerging as the only known inorganic phosphate (Pi) exporter in humans, critical for Pi homeostasis, with its activity stimulated by inositol pyrophosphate InsP8 and regulated by neuronal scaffold protein KIDINS220. Our structural studies reveal InsP8 specifically activates XPR1 in a stepwise manner, involving profound SPX domain movements. Each XPR1 subunit functions with four gating states, in which Pi permeates a constriction site via a "knock-kiss-kick" process. In contrast, KIDINS220 delicately stabilizes XPR1 in a closed conformation through multiple mechanisms, one of which involves trapping the XPR1 1 helix--critical for InsP8 binding--within an interaction hub. InsP8 serves as a key to release KIDINS220s restraint, reinforcing a "key-to-locks" mechanism to safeguard the stepwise activation. Additionally, our study provides direct structural insights into XPR1-associated neuronal disorders and highlights the evolutionary conservation and divergence among XPR1 orthologues, offering a comprehensive understanding of Pi homeostasis across species.

molecular biology↗

bioRxiv2024

Silicon-rhodamine-enabled Identification (SeeID) for Near-Infrared Light Controlled Proximity Labeling In Vitro and In Vivo

Advancement in fluorescence imaging techniques enables the study of protein dynamics and localization with unprecedented spatiotemporal resolution. However, current imaging tools are unable to elucidate dynamic protein interactomes underlying imaging observations. In contrast, proteomics tools such as proximity labeling enable the analysis of protein interactomes at a single time point but lack information about protein dynamics. We herein developed Silicon-rhodamine-enabled Identification (SeeID) for near-infrared light controlled proximity labeling that could bridge the gap between imaging and proximity labeling. SeeID was benchmarked through characterization of various organelle-specific proteomes and the KRAS protein interactome. The fluorogenic nature of SiR allows for intracellular proximity labeling with high subcellular specificity. Leveraging SiR as both a fluorophore and a photocatalyst, we developed a protocol that allows the study of dynamic protein interactomes of Parkin during mitophagy. We discovered the association of the proteasome complex with Parkin at early time points, indicating the involvement of the ubiquitin-proteasome system for protein degradation in the early phase of mitophagy. In addition, by virtue of the deep tissue penetration of near-infrared light, we achieved spatiotemporally controlled proximity labeling in vivo across the mouse brain cortex with a labeling depth of [~]2 mm using an off-the-shelf 660 nm LED light set-up.

molecular biology↗

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↗