Biology subjects

Fan, R.

Publications and source records attributed to Fan, R..

At least 55 records · Page 3Linked to original sources

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

Carbonic anhydrase 4 disruption and pharmacological inhibition reduce synaptic and behavioral adaptations following oxycodone withdrawal

The ongoing opioid crisis underscores the need for innovative treatments targeting the neurobiological mechanisms underlying opioid-seeking behaviors and relapse. Here, we explored the role of carbonic anhydrase 4 (CA4) in modulating synaptic adaptations to oxycodone withdrawal in mice. We disrupted CA4 genetically and inhibited it pharmacologically with acetazolamide (AZD), a carbonic anhydrase inhibitor used clinically. We found that oxycodone withdrawal increased AMPAR/NMDAR ratio and synaptic recruitment of calcium-permeable AMPARs in nucleus accumbens core (NAcC) medium spiny neurons (MSNs). Synaptic changes required an extended period of abstinence, generalized to other opioids, including morphine and heroin, were more pronounced in D1 dopamine receptor-expressing MSNs, and were prevented by CA4 disruption. AZD administration in vitro and in vivo reversed the synaptic alterations, and the effects of AZD depended on CA4 and acid-sensing ion channel-1A. Interestingly, abstinence from oxycodone did not affect dendritic spine density in NAcC MSNs, in contrast to previously observed effects of abstinence from cocaine. Finally, in an oxycodone self-administration paradigm, CA4 disruption and AZD reduced drug-seeking behaviors following 30 days of forced abstinence. Together, these findings identify a critical role for CA4 in synaptic adaptations in opioid withdrawn mice and drug-seeking behavior. Moreover, they suggest pharmacological inhibitors of CA4 may hold therapeutic potential for reducing opioid-seeking and relapse in opioid use disorder.

neuroscience↗

bioRxiv2025

An expanded palette of bright and photostable organellar Ca2+ sensors

The use of fluorescent sensors for functional imaging has revolutionized the study of organellar Ca2+ signaling. However, understanding the dynamic interplay between intracellular Ca2+ sinks and sources has been hindered by the lack of bright, photostable, and multiplexed measurements in different organelles, limiting our ability to define how Ca2+ shapes cell physiology across fields of biology. Here we introduce a new toolkit of chemigenetic organellar Ca2+ indicators whose color is tunable by reconstituting their fluorescence with different exogenous rhodamine dye-ligands, which significantly expand the capacity for multiplexing organellar Ca2+ measurements. These sensors, which we named ER-HaloCaMP and Mito-HaloCaMP, are optimized to report Ca2+ dynamics in the endoplasmic reticulum (ER) and mitochondria of mammalian cells and neurons, and show significantly improved brightness, photostability and responsiveness when compared to current best-in-class alternatives. Using either red or far-red dye-ligands, both ER-HaloCaMP and Mito-HaloCaMP enable visualizing ER and mitochondrial Ca2+ dynamics in neuronal axons, a subcellular location that only contains a few ER tubules and small mitochondria, structural limitations that have impaired measurements with previous red sensors. To show the expanded multiplexing capacities of our toolkit, we measured interorganellar Ca2+ fluxes simultaneously in three different subcellular compartments in live cells, revealing that the amplitude of ER Ca2+ release controls the efficacy of ER-mitochondria Ca2+ coupling in a cooperative manner. Organellar HaloCaMPs enable also measuring Ca2+ dynamics in intact brain tissue from flies and rodents, demonstrating their versatility across biological models. Our new toolkit provides an expanded palette of bright, photostable and responsive organellar Ca2+ sensors, which will facilitate future studies of intracellular Ca2+ signaling across fields of biology in health and disease.

cell 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↗

bioRxiv2024

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

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

cell biology↗

bioRxiv2024

Mapping intratumoral myeloid-T cell interactomes at single-cell resolution reveals targets for overcoming checkpoint inhibitor resistance

Effective cancer immunotherapies restore anti-tumor immunity by rewiring cell-cell communication. Treatment-induced changes in communication can be inferred from single-cell RNA-sequencing (scRNA-seq) data, but current methods do not effectively manage heterogeneity within cell types. Here we developed a computational approach to efficiently analyze scRNA-seq-derived, single-cell-resolved cell-cell interactomes, which we applied to determine how agonistic CD40 (CD40ag) alters immune cell crosstalk alone, across tumor models, and in combination with immune checkpoint blockade (ICB). Our analyses suggested that CD40ag improves responses to ICB by targeting both immuno-stimulatory and immunosuppressive macrophage subsets communicating with T cells, and we experimentally validated a spatial basis for these subsets with immunofluorescence and spatial transcriptomics. Moreover, treatment with CD40ag and ICB established coordinated myeloid-T cell interaction hubs that are critical for reestablishing antitumor immunity. Our work advances the biological significance of hypotheses generated from scRNA-seq-derived cell-cell interactomes and supports the clinical translation of myeloid-targeted therapies for ICB-resistant tumors.

systems biology↗

bioRxiv2024

Universal function of WRI1 involved in integrated regulation of multiple latex metabolism pathways in Natural Rubber

Hevea brasiliensis and Taraxacum kok-saghyz are sources of nature rubber (NR) and recent research has focused on finding a method to increase NR quality and production. NR consists mainly of cis-1,4-polyisoprene, which is synthesized from sucrose by a series of reactions during carbon metabolism. WRINKLED1 (WRI1) is a transcription factor (TF) that coordinates many genes involved in carbon metabolism and lipid biosynthesis. Here, we isolated and characterized two orthologues of WRI in rubber-producing plants H. brasiliensisa and T. kok-saghyz, which are highly expressed in their latex. Subcellular localization and ectopic expression in Arabidopsis thaliana indicated that the TFs, HbWRI1 and TkWRI1, are involved in lipid accumulation. Overexpression of HbWRI1 and TkWRI1 in T. kok-saghyz substantially enhanced NR quality and production, including dry rubber content, molecular weight, and the diameter of rubber particles in the latex. Conversely, they were decreased in TkWRI1 repression. Furthermore, based on the activated expression in transgenic T. kok-saghyz latex and the existence of AW-box element in the promoter regions, 16 direct downstream genes of HbWRI1 and TkWRI1 were identified by a dual-luciferase reporter assay and yeast one-hybrid assay, and their products were responsible for both NR biosynthesis and lipid metabolism. These results reveal a regulatory module that HbWRI1 and TkWRI1 TFs can positively regulate NR quality and production in T. kok-saghyz latex by synergistically regulating the entire NR biosynthesis. This module is expected to be utilized to develop superior varieties to enhance NR quality and production.

biochemistry↗

bioRxiv2024

Mosaic integration of spatial multi-omics with SpaMosaic

With the advent of spatial multi-omics, mosaic integration of diverse datasets with partially overlapping modalities enables construction of comprehensive multi-modal spatial atlases from heterogeneous sources. Here, we present SpaMosaic, a tool that employs contrastive learning and graph neural networks to build a modality-agnostic and batch-corrected latent space for spatial domain identification and missing modality imputation. We systematically benchmarked SpaMosaic against existing integration methods using simulated data and experimentally acquired datasets spanning RNA and protein abundance, chromatin accessibility, and histone modifications from brain, embryo, tonsil, and lymph node tissues. SpaMosaic consistently outperformed other methods in identifying coherent spatial domains by reducing noise and mitigating batch effects across diverse technologies and developmental stages. Computationally, SpaMosaic is highly scalable, capable of integrating over 100 sections and processing a single section with more than 800,000 spots. Beyond robust integration, the unified latent space generated by SpaMosaic enables accurate imputation of missing modalities. In a mosaic mouse brain dataset, the imputed histone modifications not only recapitulated expected transcriptome-epigenome correlations but also uncovered more region-specific regulatory links compared to the measured chromatin accessibility data, demonstrating the ability to infer relationships between modalities without co-profiling. In summary, SpaMosaic provides a versatile framework for unifying the rapidly accumulating heterogeneous spatial omics data into comprehensive biological atlases.

bioinformatics↗

bioRxiv2024

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping

The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag- RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.

neuroscience↗

bioRxiv2024

High-motility pro-tumorigenic monocytes drive macrophage enrichment in the tumor microenvironment

Enrichment of tumor-associated macrophages (TAM{Phi}s) in the tumor microenvironment correlates with worse clinical outcomes in triple-negative breast cancer (TNBC) patients, prompting the development of therapies to inhibit TAM{Phi} infiltration. However, the lackluster efficacy of CCL2-based chemotaxis blockade in clinical trials suggests that a new understanding of monocyte/macrophage infiltration may be necessary. Here we demonstrate that random migration, and not only chemotaxis, drives macrophage tumor infiltration. We identified tumor- associated monocytes (TAMos) that display a dramatically enhanced migration capability, induced rapidly by the tumor microenvironment, that drives effective tumor infiltration, in contrast to low-motility differentiated macrophages. TAMo, not TAM{Phi}, promotes cancer cell proliferation through activation of the MAPK pathway. IL-6 secreted both by cancer cells and TAMo themselves enhances TAMo migration by increasing dendritic protrusion dynamics and myosin- based contractility via the JAK2/STAT3 signaling pathway. Independent from CCL2 mediated chemotaxis, IL-6 driven enhanced migration and pro-proliferative effect of TAMo were validated in a syngeneic TNBC mouse model. Depletion of IL-6 in cancer cells significantly attenuated monocyte infiltration and reversed TAMo-induced cancer cell proliferation. This work reveals the critical role random migration plays in monocyte driven TAM{Phi} enrichment in a tumor and pinpoints IL-6 as a potential therapeutic target in combination with CCL2 to ameliorate current strategies against TAM{Phi} infiltration.

immunology↗

bioRxiv2024

SPACEc: A Streamlined, Interactive Python Workflow for Multiplexed Image Processing and Analysis

Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.

bioinformatics↗

bioRxiv2024

Periodic ER-plasma membrane junctions support long-range Ca2+ signal integration in dendrites

Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at [~]1 m intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca2+ channels and ER Ca2+-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca2+ homeostasis, and local activation of the Ca2+/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca2+ modulatory machinery facilitating voltage-independent signal transmission and ryanodine receptor-dependent Ca2+ release at ER-PM junctions over 20 m away. Thus, interconnected ER-PM junctions support signal propagation and Ca2+ release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations. HighlightsO_LIPeriodic ER-PM junctions tile neuronal dendritic plasma membrane in rodent and fly. C_LIO_LIER-PM junctions are populated by ER tethering and Ca2+ release and influx machinery. C_LIO_LIER-PM junctions act as sites for local activation of CaMKII. C_LIO_LILocal spine activation drives Ca2+ release from RyRs at ER-PM junctions over 20 m. C_LI

neuroscience↗

bioRxiv2024

MedImg: a database for public medical image integration

The advancements of deep learning algorithms in medical image analysis has garnered tremendous attention in recent years. Several studies have reported that the models have achieved and even surpassed human performance, whereas the translation of these models into clinical practice is still accompanied by various challenges. A major challenge is the large-scale and well characterized dataset to validate the generalization of models. Therefore, we collected diverse medical image datasets from multiple public sources containing 103 datasets, 1,622,956 images. These images are derived from 14 modalities like XR, CT, MRI, OCT, ultrasound, and endoscopy, and from 9 organs such as lung, brain, eye, and heart. Subsequently, we constructed an online database, MedImg, which incorporates and hierarchically organizes medical images to facilitate data access. MedImg serves as an intuitive and open-access platform for contributing to deep learning-based medical image analysis, accessible at https://www.cuilab.cn/medimg/.

bioinformatics↗

bioRxiv2024

Estimating the selection pressure and evolutionary rate of proteins on the non-neutral hypothesis of synonymous mutations

The Nonsynonymous/Synonymous substitution rate ratio (Ka/Ks) is a commonly used metric to estimate the selection pressure and evolutionary rate of proteins in comparative genomics, which plays critical roles in molecular evolution in both biology and medicine. A fundamental assumption of Ka/Ks is that synonymous mutations are evolutionarily neutral and not subject to natural selection as they do not alter protein sequences and function. However, a number of studies have demonstrated that synonymous mutations are non-neutral and may lead to diseases through a number of mechanisms, such as altering miRNA regulation. This further implies that synonymous mutations also participate in the process of natural selection and thus Ka/Ks should be redefined as well. For this purpose, here we propose an improved Ka/Ks ratio, iKa/Ks, which re-computed the neutral substitution rate by taking the altered status of miRNA binding into consideration, and thereby incorporate the impact of synonymous mutations on miRNA regulation. As a result, iKa/Ks shows better performance than Ka/Ks when comparing them using their correlation with expression distance. Moreover, case studies showed that iKa/Ks is able to identify the positive/negative selection genes that are missed by Ka/Ks. For example, TMEM72/Tmem72 is estimated to be positively selected by iKa/Ks (1.13) but negatively selected by the conventional Ka/Ks ratio (0.21). Further evidence showed its rapid evolution, which further support the power of the new algorithm.

bioinformatics↗

bioRxiv2024

Comprehensive prediction and analysis of human protein essentiality based on a pre-trained protein large language model

Human essential genes and their protein products are indispensable for the viability and development of the individuals. Thus, it is quite important to decipher the essential proteins and up to now numerous computational methods have been developed for the above purpose. However, the current methods failed to comprehensively measure human protein essentiality at levels of humans, human cell lines, and mice orthologues. For doing so, here we developed Protein Importance Calculator (PIC), a sequence-based deep learning model, which was built by fine-tuning a pre-trained protein language model. As a result, PIC outperformed existing methods by increasing 5.13%-12.10% AUROC for predicting essential proteins at human cell-line level. In addition, it improved an average of 9.64% AUROC on 323 human cell lines compared to the only existing cell line-specific method, DeepCellEss. Moreover, we defined Protein Essential Score (PES) to quantify protein essentiality based on PIC and confirmed its power of measuring human protein essentiality and functional divergence across the above three levels. Finally, we successfully used PES to identify prognostic biomarkers of breast cancer and at the first time to quantify the essentiality of 617462 human microproteins. Key PointsO_LIPIC outperformed existing computational methods for predicting essential proteins. C_LIO_LIPIC could comprehensively predict human protein essentiality at levels of human, human cell lines and mice orthologues at the same time. C_LIO_LIPES could serve as a potential metric to quantify the essentiality of both human proteins and human microproteins. C_LI

bioinformatics↗

bioRxiv2024

Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning

Spatial omics allow us to identify and analyze communities of cells coordinating specific functions within a tissue. While these communities, defined as cell niches, are fundamentally shaped by interactions between spatially neighboring cells, we lack computational frameworks that can leverage spatial omics data to quantitatively characterize niches based on cell interaction events. To address this, we introduce NicheCompass, a graph deep learning method designed based on the principles of cellular communication. NicheCompass not only identifies cell niches, but also learns and informs about the signaling events shaping the identity of these niches. Unlike existing methods, it uniquely characterizes niches by quantifying their activity of spatial gene programs which represent diverse mechanisms of cell-cell communication and transcriptional regulation, thereby uncovering the underlying cellular processes constituting each niche. We showcase a comprehensive workflow encompassing data integration, niche identification, and functional interpretation, and demonstrate that, with its biologically informed design, NicheCompass outperforms existing methods. NicheCompass is broadly applicable to spatial transcriptomics data, which we illustrate by mapping the architecture of diverse tissues during mouse embryonic development, and delineating basal (KRT14) and luminal (KRT8) tumor niches in human breast cancer. We further introduce fine-tuning-based spatial reference mapping, revealing an SPP1+ macrophage-dominated tumor niche in non-small cell lung cancer patients. Additionally, we extend NicheCompass to multimodal spatial profiling of gene expression and chromatin accessibility, identifying and characterizing distinct white matter niches in the mouse brain. Finally, we apply NicheCompass to a whole mouse brain spatial atlas with 8.4 million cells demonstrating its scalability and ability to build foundational, interpretable spatial representations for entire organs. Overall, NicheCompass provides a novel approach to the challenge of identifying and analyzing niches, and suggests a more rigorous niche definition grounded in the quantitative characterization of underlying cellular processes.

bioinformatics↗

bioRxiv2024

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

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

systems biology↗