Biology subjects

Zhang, D.

Publications and source records attributed to Zhang, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Affinity Maturation Enables Dual Targeting of Cancer-Associated MUC1-Tn/STn and Drives Potent Tumor Control

A major challenge in solid tumor immunotherapy is identifying tumor-restricted antigens while overcoming intratumoral antigen heterogeneity. The truncated O-glycans Tn (GalNAc-O-Ser/Thr) and STn (Neu5Ac2-6GalNAc-O-Ser/Thr) are aberrantly displayed on the transmembrane glycoprotein MUC1 across a broad spectrum of epithelial malignancies, making MUC1-Tn/STn an attractive shared tumor-associated antigen class. However, heterogeneous intratumoral Tn/STn expression renders single-glycoform-specific antibodies vulnerable to antigen-driven escape. To address this challenge, we used structure-guided directed evolution and yeast surface display to affinity-mature the anti-MUC1-Tn antibody 5E5, generating variants with high-affinity dual recognition of MUC1-Tn and MUC1-STn. Co-crystal structures revealed that heavy-chain CDR loops cooperatively engage the GalNAc moiety primarily through hydrogen-bonding networks, in which HCDR3 residue 98 fine-tunes glycoform preference: Tyr98 favors MUC1-Tn recognition, whereas His98 enhances MUC1-STn engagement, while both support high-affinity recognition of both glycoforms. When reformatted as bispecific T-cell engagers, the affinity-matured variants mediated potent and durable cytolytic activity against cancer cells with heterogeneous glycan profiles, suppressed mixed Tn/STn tumors in vivo where the parental antibody failed, and showed enhanced activity in patient-derived tumor-like clusters. Together, these findings establish functional glycoform coverage as a design principle for antibody engineering, whereby a single paratope is optimized to recognize a defined spectrum of tumor-associated glycoforms while preserving antigen specificity, providing a strategy to overcome glycan heterogeneity in solid tumor immunotherapy.

cancer biology↗

bioRxiv2026

Early-life core microbiota are associated with postlarval survival of Penaeus vannamei: A meta-analysis

Larviculture is critical to the production of Penaeus vannamei, the world's leading farmed shrimp species, but remains constrained by erratic postlarval survival. Although early-life microbiome establishment is increasingly recognized as essential for larval health and survival, microbiome-based hatchery interventions are hindered by limited understanding of larval core microbiota. Here, we synthesized publicly available and self-generated 16S rRNA gene amplicon datasets of larval and rearing-water microbiota from 13 studies encompassing seven major producing regions worldwide to characterize shrimp larval core microbiota, their assembly processes, and their associations with larval survival. Although larval prokaryotic communities exhibited stronger geographic variation than developmental-stage succession, a conserved set of dominant bacterial families, including Roseobacteraceae, Vibrionaceae, Flavobacteriaceae, Moraxellaceae, and Pseudoalteromonadaceae, consistently prevailed across developmental stages and regions. By integrating occupancy, persistence, and ecological network centrality analyses, we demonstrated that common, temporal, and ecological core microbiota differ substantially in composition, diversity, stage specificity, and ecological roles in network stability. Compared with non-core taxa, core taxa were generally assembled more deterministically, irrespective of core category, suggesting stronger host selection and/or more rapid dispersal during microbiome establishment. Core microbiota also generally showed stronger associations with postlarval survival than total communities or non-core taxa, although contrasting survival associations among core members indicated that core microbiota are not ecologically homogeneous. Our work provides the first systematic categorization and characterization of larval core microbiota throughout the shrimp larviculture cycle, offering new insights into the ecological significance of core microbiota and identifying potential universal targets for microbiome-based intervention in shrimp larviculture.

microbiology↗

bioRxiv2026

NeuroPilot: Longitudinal intravital neural imaging across scales and modalities through an agentic platform

Longitudinal intravital imaging is essential for understanding how neural populations evolve across behavioral states, yet converting large-scale recordings into biological insight remains a fundamental challenge because neural signals are degraded by noise, non-rigid motion, and heterogeneous recording conditions. Here we introduce NeuroPilot, an agentic framework that transforms neural imaging analysis into a closed-loop, context-aware process, integrating a multimodal large language model with callable computational imaging modules to adaptively coordinate workflows from raw recordings to population-level readouts. Across heterogeneous imaging and experimental regimes, NeuroPilot restores motion-degraded recordings and supports consistent neural signal recovery, detection, and tracking in challenging deep-brain, freely moving, and longitudinal settings. These enable neuronal population tracking across spinal stimulation paradigms, deep-brain imaging, and continuous 24-hour mPFC recordings aligned with spontaneous sleep-wake behavior. By converting heterogeneous intravital recordings into stable population activity maps, NeuroPilot supports behavioral decoding and reveals structured neural dynamics across behavioral states and transitions. NeuroPilot establishes a scalable agentic paradigm for neural imaging analysis, expanding experimental access to the measurement, visualization, and interpretation of neural population activity.

neuroscience↗

bioRxiv2026

Spatially resolved host-fungal transcriptomics profiling by fungal-DBiT

Hosts and their associated fungi coexist within a shared tissue environment, engaging in dynamic interactions that influence immune regulation, tissue homeostasis, and disease progression. Dissecting these interactions requires spatially resolved and unbiased approaches capable of capturing gene expression from both host and fungal cells within intact tissue architecture. Here, we present fungal-DBiT, a spatial transcriptomic platform that enables simultaneous, high-resolution mapping of host and fungal RNAs, directly in tissue sections. Using fungal-DBiT, we profiled the spatial distribution of host and fungal transcripts in a fungal infection mouse model, and review pronounced spatial heterogeneity in fungal localization and activity, accompanied by region-specific host transcriptional responses. We further applied fungal-DBiT to intestinal tissues under physiological conditions, demonstrating its sensitivity in detecting low-abundance fungal species in the absence of overt inflammation. Together, these results demonstrate the ability of fungal-DBiT to resolve host-fungal interactions in situ and highlight its utility for studying mycobiome associated immune responses and disease processes with spatial precision.

microbiology↗

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

The dark transcriptome mouse organogenesis cell atlas

Single-cell atlases have transformed our understanding of mammalian tissue biology, yet the diverse noncoding "dark transcriptome" remains largely uncharted. Here, we present the Dark Transcriptome Mouse Organogenesis Cell Atlas (DT-MOCA), comprising 276,455 cells across embryonic days 11 to 18 and jointly capturing protein-coding transcripts and five major classes of noncoding RNAs through single-nucleus total RNA sequencing, establishing the first comprehensive catalog of noncoding RNAs across major cell types and developmental lineages. We found that long noncoding RNA and microRNA profiles distinguished major cell types and developmental lineages with resolution comparable to messenger RNA, whereas transfer RNA, small nuclear RNA, and small nucleolar RNA programs were less cell type-discriminative and more broadly shared. We further resolve noncoding RNA expression patterns as lineage-specific, temporally modulated, or coordinately regulated by cell identity and developmental stage. We observed that temporally regulated long noncoding RNAs were evolutionarily younger than those associated with cell identity. We further generate a catalog of the change of noncoding RNAs cross all 11 major developmental trajectories. It revealed lineage-specific coordination between transfer RNA supply and codon demand during musculoskeletal organogenesis as well as dynamic microRNA and small nucleolar RNA programs during the central nervous system development. We observed microRNAs converge on regulators of proliferation and gliogenesis and shared induction of Snord116 defining a developmental window relevant to Prader Willi syndrome. Together, this atlas provides a comprehensive catalog of the developmental dark transcriptome and reveals how noncoding RNA programs are organized across cell identity, developmental time, and lineage progression.

developmental biology↗

bioRxiv2026

Simultaneous multi-area recordings in macaque frontoparietal cortex during a manual object-interaction task

Dexterous hand control is essential for daily activities and a top restoration priority for people with tetraplegia, yet neurophysiological datasets suitable for studying and decoding naturalistic hand behavior remain scarce. Most available recordings constrain movements to a small number of degrees of freedom or cover only a single stereotypical grasp-and-hold structure. Moreover, previous brain-computer interface (BCI) studies have largely relied on signals from a single cortical area, even though dexterous hand control is distributed across an interconnected frontoparietal network. To address these limitations, we designed a paradigm that elicits rich hand movements across 40 distinct hand-object interaction conditions. We recorded simultaneous frontoparietal activity with hand kinematics from a rhesus macaque performing them. Neural activity was recorded from the primary motor cortex, anterior intraparietal area, and ventral premotor cortex using microelectrode arrays spanning 1,920 channels, while a multi-camera markerless motion-capture system acquired video from which kinematics were extracted offline. The dataset comprises data from 20 sessions, including processed neural features, synchronized 3D coordinates of hand and elbow keypoints, and trial-level metadata including task conditions and event timing. We further provide technical validation, including a neural decoding benchmark with multiple algorithms for continuous keypoint coordination reconstruction. We found that cross-day and cross-task decoding performance varied among algorithms. Variation in channel availability leads to different performance degradation curves in different algorithms. This task-rich dataset offers a valuable resource for investigating the neural mechanisms of dexterous hand control and for advancing the performance of motor BCIs.

neuroscience↗

bioRxiv2026

Cell type-resolved connectomic reconstruction of the spinal dorsal horn reveals sensory neuron synaptic organization and modality-specific inhibition

Touch, temperature, chemical, and noxious stimuli are detected by more than a dozen peripheral sensory neuron subtypes that together mediate somatosensation. Somatosensory neurons form synapses in the spinal cord dorsal horn, the first site of central somatosensory processing, to engage the downstream circuitry that drives both spinal reflexes and ascending pathways for perception. Yet how sensory neuron synapses are organized into the spinal cord circuitry remains poorly understood. Here we present two connectomic tools for studying somatosensory circuits of the spinal cord dorsal horn: dSC1, a large-scale, synapse-resolved electron microscopy (EM) dataset of the mouse dorsal horn, and SPINE (SPinal cord Integrated NEuron classifier), a deep-learning pipeline that identifies major sensory neuron subtypes directly from EM images. Applying SPINE to the dSC1 dataset, we found marked heterogeneity in synaptic architecture across physiologically distinct sensory neuron subtypes: low-threshold mechanoreceptors form substantially more synapses and receive far more GABAergic presynaptic inhibition than high-threshold mechanoreceptors, thermoreceptors and nociceptors. Cell-type-resolved circuit reconstruction further identified five populations of spinal inhibitory neurons (IhNs) that form synapses onto sensory neuron axons, each preferentially driven by and presynaptically inhibiting a single sensory subtype - a homotypic, modality-specific inhibitory motif. Most of these IhNs also feedforwardly inhibit other sensory pathways - a heterotypic, cross modality motif. Together, dSC1 and SPINE enable cell-type-resolved spinal cord circuit reconstruction and reveal a dual-mode logic of sensory-evoked inhibition that shapes modality-specific signaling at the earliest stage of somatosensory processing.

neuroscience↗

bioRxiv2026

A synaptic wiring diagram underlying modality-specific ascending pathways of the superficial spinal dorsal horn

The spinal cord dorsal horn transforms tactile, thermal, pruritic, and nociceptive signals from peripheral somatosensory neurons into outputs to the brain, but how dorsal horn circuits organize diverse inputs into modality-selective ascending pathways is poorly understood. Using dSC1, a large-scale electron microscopy dataset of the mouse dorsal horn, we mapped the circuitry connecting primary sensory afferents to projection neurons of the anterolateral tract (ALT), a principal pathway conveying somatosensory information to the brain. We find that afferents encoding distinct modalities are routed onto connectivity-defined populations of ALT projection neurons. Nociceptors and cold thermoreceptors directly innervate distinct lamina I ALT projection neuron populations specialized for conveying noxious and cooling signals, respectively, whereas tactile and pruritic signals instead pass through a recurrent, multilayered network involving interneurons called vertical cells before reaching a separate ALT projection population. This distinction, direct versus polysynaptic wiring, underlies marked differences in sensory convergence and establishes segregated ascending channels for conveying signals encoding the principal somatosensory modalities. Together, our findings define an architectural logic by which the dorsal horn integrates and routes tactile, thermal, pruritic, and nociceptive information into distinct ALT output pathways to the brain.

neuroscience↗

bioRxiv2026

Consensus Formation History Shapes Collective Updating

Consensus is rarely a one-off achievement. In a changing world, groups must repeatedly update judgments they have already agreed upon, making consensus updating a common feature of real-world collective decisions. However, previous research on collective decision-making has focused mainly on how groups reach a single consensus, leaving the updating of established consensus largely unexamined. This raises two unanswered questions: does consensus updating involve biases analogous to sunk cost, and are such biases related to the cost of the previous consensus? Building a Fake Consensus paradigm, we manipulated consensus-formation cost and examined how dyads updated a subjectively established consensus after new information arrived. An independent individual experiment provided information-matched updating baselines, allowing us to determine whether dyads updated less or more than individuals responding to the same evidence. Contrary to sunk-cost predictions, low-cost consensus led dyads to update less than the individual baseline, whereas medium- and high-cost consensus led them to update more. The pathway through perceived consensus sharedness was significant, and the association between sharedness and updating was stronger at higher left temporoparietal junction inter-brain synchrony. These findings indicate that the history of consensus formation shapes subsequent updating by altering the psychological meaning of shared endorsement, while interpersonal alignment determines how this shared experience translates into coordinated behavior. To our knowledge, this is the first experimentally controlled study of consensus updating in teams to isolate formation history from actual disagreement, providing a new paradigm for studying how collective judgments change after agreement.

neuroscience↗

bioRxiv2026

Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT

Spatial omics technologies provide unprecedented opportunities to investigate how molecular programs are organized within tissues. However, existing analytical frameworks primarily focus on identifying cellular states and characterizing differentially expressed molecular features across these states. The relational spatial organization of molecular features and its dynamics across biological contexts therefore remain largely unexplored. Here, we present GeneSPOT, a gene-centric computational framework for characterizing the spatial relationships among genes and quantifying their reorganization across biological conditions. Although gene-centric in design, GeneSPOT generalizes to other molecular features. Across diverse applications, GeneSPOT resolved tissue structures, integrated paired and unpaired spatial multi-omics data, unveiled the spatial convergence of gene programs during embryonic development, and identified aging-associated spatial reorganization among genes whose expression did not change significantly with age. GeneSPOT thus complements conventional cell-centric analysis, providing a framework for deciphering how spatial relationships among molecular features are reorganized across biological conditions and over time.

bioinformatics↗

bioRxiv2026

Platform-specific count-matrix preprocessing workflows for spatial transcriptomics data analysis

Preprocessing of spatial transcriptomics (ST) count matrices is critical for removing technical variation while preserving biological signals, but optimal strategies remain unclear across diverse platforms. We systematically benchmarked 371 preprocessing workflows across 45 data spanning 11 mainstream ST platforms, evaluating their performance using specifically designed complementary metrics. To further assess preprocessing factors upstream of count-matrix generation, we additionally evaluated image preprocessing, including cell segmentation and post-segmentation transcript processing, across representative imaging-based ST platforms. We found that no universal count-matrix preprocessing workflow performed optimally across all platforms, while data from the same platforms exhibited striking consistency in optimal count-matrix preprocessing workflows. We identified the key data structure characteristics and count-matrix preprocessing steps that critically influence preprocessing performance. We developed platform-specific and data structure-guided recommendations to assist users in selecting the optimal count-matrix preprocessing workflow, validated across a broader range of downstream tasks. We demonstrated that our data-driven recommendations uncover subtle biologically meaningful spatial patterns in new data that were obscured by suboptimal count-matrix preprocessing. Our findings suggest best practices for ST data count-matrix preprocessing.

bioinformatics↗

bioRxiv2026

Aggregated Low-Density Lipoprotein (agLDL) Triggers Inflammatory Responses in Macrophages through Toll-Like Receptor 4 (TLR4)

Atherosclerosis is a chronic inflammatory disease primarily initiated by the sub-endothelial retention and modification of low-density lipoproteins (LDL), such as oxidation and aggregation. While oxidized LDL (oxLDL) exists in atherosclerotic lesions, aggregated lipoproteins are also prominent. Aggregated LDL (agLDL), for instance, is viewed as a passive substrate for macrophages to uptake and form foam cells. Recent studies show that agLDL, through toll-like receptor 4 (TLR4), induces formation of lysosomal synapses, which drive agLDL degradation by macrophages, a process described as "digestive exophagy". As TLR4 is one of the pattern-recognition receptors for inflammatory responses in macrophages, we postulated that agLDL could initiate macrophage inflammation along with lipoprotein degradation/internalization, thereby contributing to a low degree of chronic inflammation in atherosclerosis. Here, we demonstrate that agLDL directly activates the canonical NF-{kappa}B signaling pathway and triggers a pro-inflammatory transcriptional program through TLR4, which closely resembles classical lipopolysaccharide (LPS)-mediated TLR4 activation. In addition, agLDL also activates TLR4 to generate inflammatory macrophages that recruit immune cells, a key process in the development of atherosclerotic lesions. Our findings therefore support a novel dual functionality of agLDL: a lipid donor and an inflammatory agonist. This establishes the agLDL-TLR4-NF-{kappa}B axis as a direct link between LDL aggregation in the intima and the initiation of sterile vascular inflammation. It also strengthens the understanding of the role of digestive exophagy with new mechanistic details.

cell biology↗

bioRxiv2026

De novo design of flexible protein interactions with GuideFlip

De novo design of protein binders requires a target structure. However, for flexible targets, such as intrinsically disordered proteins, this structure does not exist until the binder has stabilized the interaction. Such targets are therefore difficult for methods that separate structure generation from sequence design. We introduce GuideFlip, which co-designs structure and sequence through guided discrete flow matching: binder residues are assigned progressively while the complex is re-predicted at each step, allowing the evolving interface to affect the design process. GuideFlip reduces the hydrophobic bias of direct AlphaFold optimization and improves in silico success rates over existing approaches. We release a database of binder candidates for 177 human disordered proteins. Experimentally, we obtain de novo binders to the C-terminus of -synuclein and the disordered amino terminus of RBX1 with hit rates of 13.5% and 41.7%, respectively, and we confirm the epitopes of selected binders by NMR and mutagenesis. Applying GuideFlip to flexibility on the binder side, we design a nanobody that binds the agonist-bound {beta}1-adrenergic receptor in the active state, but not the receptor in its inactive state, with a 75% hit rate and cryo-EM structure confirming the design. GuideFlip enables protein design where bound structures emerge only upon binding.

biophysics↗

bioRxiv2026

Ultra-fast and scalable high-resolution full-length single-cell RNA sequencing using CHART-seq

Plate-based full-length single-cell RNA sequencing resolves transcript structure details but remains difficult to scale because each cell usually requires a separate library. Here we developed CHART-seq (Combinatorial Heteroduplex Assay via Recombinant Tn5), which uses orthogonally indexed Tn5 complexes to tagment RNA/cDNA heteroduplexes and permits early sample pooling. The workflow processed up to 96 cells per library, was compatible with 384-well expansion, and completed library preparation within 3 h at a reagent cost below US$1 per cell. At matched sequencing depth, CHART-seq detected more genes and annotated isoforms than Smart-seq2, Smart-seq3, Flash-seq and SHERRY2, while retaining broad genebody coverage and reproducible expression estimates. In the CHART-seq results of vascular smooth muscle cells, TGF-{beta}1 pretreatment before PDGF-BB exposure partly restored contractile features, suppressed a PDGF-associated inflammatory programme, and induced a distinct metabolic-matrix response with coordinated transcript-usage changes. These biological findings remain exploratory because independent biological replicates were unavailable. CHART-seq provides a rapid, scalable route to full-length single-cell transcript profiling with gene-programme and candidate isoform resolution.

genomics↗

bioRxiv2026

ARCHER-LD: Rapid Long-Range Linkage Disequilibrium Calculations at Biobank Scale using GPU Acceleration

Linkage disequilibrium (LD) information from one's own dataset is considered optimal for downstream analyses such as statistical fine-mapping. However, the computational complexity ({approx}N2 / 2 computations for N variants) lead most studies to use external reference panels, such as from 1000 Genomes. To capture LD across all variant pairs in biobank-scale whole-genome sequencing (WGS) datasets with hundreds of millions of variants, new computational strategies are essential. We present a novel approach that uses multi-GPU distributed computing to compute R2 for every variant pair in a dataset. On chromosome 22 (1.8 million variants) of the 30x WGS 1000 Genomes dataset, our method using eight consumer-level 12GB GPUs (NVIDIA RTX 2080TIs) takes <10 minutes, while the same calculation with PLINK using a high-end 64-threaded CPU (Intel Xeon Gold 6338) takes >80 minutes, corresponding to a {approx}8x speedup. We further demonstrate true biobank-scale performance in the Penn Medicine Biobank (PMBB; 57,170 samples), computing chromosome 1 LD ({approx}1.38 million variants) in 49 minutes versus 22.7 hours for PLINK, a {approx}28x speedup. With this method, we successfully computed on the aforementioned 30x WGS 1000 Genomes dataset ({approx}120 million variants and {approx}2500 samples) the entire LD matrix (>1e16, or 10 quadrillion elements) in under 6 hours using 512 NVIDIA 40GB A100 GPUs on the Department of Energy Argonne Leadership Computing Facility Polaris Supercomputer. We make this tool, coded in Python using CuPy, publicly available. Using this tool, researchers can leverage the full extent of their genomic data without relying on external LD reference panels and acquire more accurate, population-specific findings, particularly for groups underrepresented in existing databases.

bioinformatics↗

bioRxiv2026

Oral Actinobacteria sense and defend against parasitic epibionts

Bacteria contend with a wide range of antagonistic interactions from neighboring microbes. One widespread but poorly understood threat is posed by Patescibacteria, which proliferate by colonizing host bacteria surfaces and extracting cellular resources. Despite the ubiquity of Patescibacteria in the environment, how hosts defend against these epibionts remains unknown. Here we show that Actinomycetota fend off Patescibacteria using the Esx secretion system. We identify a secreted effector protein that mediates this defensive behavior and demonstrate that Esx induction is part of a multifaceted, epibiont-specific response that includes multiple predicted cell surface modifications. Finally, we show that this response occurs at the transcriptional level and is mediated by a previously undescribed threat sensing pathway whose activation leads to threonine phosphorylation of an FHA domain-containing output protein. Together with bioinformatic evidence linking this threat sensing pathway to Esx across Actinomycetales, our experimental findings establish a functional role for the elusive and broadly distributed Esx pathway in defense against epibionts. Furthermore, they help resolve the ecological relationship between Patescibacteria and their hosts as one of antagonism, not mutualism.

microbiology↗

bioRxiv2026

Unifying physical and molecular coordinate systems across modalities in spatial biology

Establishing a unified physical and molecular coordinate system from fragmented multi-modal data is a longstanding challenge in biology. Here, we present MAPS, a modality-agnostic platform for spatial biology comprising (1) MAPS-alignment for ultrafast alignment of any modality, (2) MAPS-integration for both anchored and unanchored integration across orthogonal modalities for 3D multi-modal reconstruction, and (3) MAPS-Explorer for large-scale interactive 3D analysis. MAPS outperformed existing methods across extensive benchmarks on 34 datasets spanning 16 technology platforms and 6 modalities, while delineating fine-grained multi-modal tissue architectures across diverse biological systems in mouse and human. At cross-consortium scale, MAPS integrated 434 slices comprising 21 million cells from 18 atlases and 5 modalities to construct the most comprehensive 3D multi-modal mouse brain atlas. At individual laboratory scale, MAPS empowered routine 2D spatial assays to reconstruct continuous 3D multi-modal landscapes of human hepatocellular carcinoma, revealing the limitations of 2D spatial relationships and uncovering depth-dependent immune-state transitions.

bioinformatics↗