Biology subjects

Fu, Y.

Publications and source records attributed to Fu, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

CBX3 Deletion Overcomes Ferroptosis Resistance in Colorectal Cancer by Targeting IFNγ/SLC7A11/SLC3A2 Axis

System Xc- (SLC7A11/SLC3A2) maintains redox balance by supporting glutathione synthesis, thereby preventing lipid peroxidation and ferroptosis, and represents a promising therapeutic target in cancer. In this work, we revealed that an important epigenetic regulator, CBX3 deletion significantly increased intracellular ROS and lipid ROS level following ferroptosis inducing, particularly under IFN{gamma} stimulation, thereby sensitized CRC cells to ferroptosis in vitro using human CRC cells as well as in vivo using MC38 syngenetic tumors. RNAseq with mice syngeneic tumors revealed an 11-gene oxidative stress signature downregulated in Cbx3KO tumors after combined treatment; its low expression score correlated with improved overall survival in CRC patients. Among them, Slc7a11, Ptgs2, and Cth were identified as CBX3 direct target genes. Additionally, several amino acid transporters functionally coupled to Xc- antiporter, as Slc3a2, Slc7a5, Slc1a5 and Slc6a9, all significantly decreased in Cbx3KO tumor under RSL3+IFN{gamma} treatment. Mechanistically, CBX3 binds directly to the promoter of Slc7a11 or Slc3a2 to sustain their expression and redox balance to resist ferroptosis in CRC. Knockout of CBX3 in CRC cells significantly impaired RNA polymerase II recruitment to the promoter of SLC7A11 or SLC3A2 and reduced SLC7A11 and SLC3A2 expression, an effect that was further exacerbated under IFN{gamma} stimulation but restored by STAT1 deletion. Overall, CBX3 protects CRC from oxidative stress by maintaining the functional integrity of IFN{gamma}/STAT1/SLC7A11/SLC3A2 axis, positioning CBX3 as a potential therapeutic target for enhancing ferroptosis therapies in CRC.

cancer biology↗

bioRxiv2026

A life-history axis of resource acquisition in bacteria: depolymerisation versus uptake

Microbial decomposition of complex organic matter is a major control on the global carbon cycle, and its efficiency hinges on how microbes acquire resources. Decomposition requires costly extracellular enzymes to break down complex polymers, whereas the resulting soluble products are captured far more cheaply through membrane transporters. How microbes balance investment between these two processes is a fundamental axis of microbial life history, yet it has remained difficult to quantify across their vast diversity. Using genomic repertoires for carbohydrate depolymerisation and uptake, we developed a simple ratio distinguishing depolymerisation-optimised from uptake-optimised strategies, and applied it to more than 249,000 environmental and host-associated bacterial genomes. These strategies were strongly phylogenetically structured and differed by habitat, with uptake-optimised bacteria unexpectedly more prevalent in diffusion-limited soils than in aquatic environments. In an 18-month field-scale plant litter decomposition experiment, uptake-optimised bacteria dominated during early decomposition, when fungi were more abundant and strongly expressed hydrolytic enzymes, shifting towards depolymerisation-optimised bacteria at later stages. A simple genomic ratio therefore captures ecologically meaningful resource-acquisition strategies that vary with phylogeny, habitat and successional stage. The balance of these strategies could shape community-level extracellular enzyme investment, with consequences for decomposition and carbon cycling.

microbiology↗

bioRxiv2026

Alpha-synuclein and neuromelanin converge to promote Parkinsons disease pathology

Parkinsons disease (PD) is defined by the preferential degeneration of neuromelanin (NM)-containing dopamine-producing neurons in the substantia nigra (SN), accompanied by intraneuronal inclusions rich in alpha-synuclein (aSYN). However, the mechanistic relationship between NM accumulation and aSYN pathology remains unclear. Here, we reveal a bidirectional pathogenic relationship between aSYN and NM. In human post-mortem midbrain tissue, ventral nigral neurons with high NM content display increased aSYN mRNA expression and are preferentially lost in PD. In a rat model that recapitulates age-dependent, human-like NM accumulation via targeted tyrosinase (TYR) expression, co-expression of aSYN accelerates NM buildup, enhances aSYN oligomerization, and aggravates nigrostriatal degeneration. Mechanistically, aSYN oligomerization in NM-producing rats decreases vesicular dopamine storage and impairs vesicle fusion with the plasma membrane, increasing cytosolic dopamine availability for oxidation into NM. Together, these findings support a pathogenic feedback loop in which elevated aSYN accelerates NM synthesis through disrupted dopamine compartmentalization, while NM accumulation further promotes aSYN aggregation and neuronal loss. This aSYN-NM axis may underlie the preferential vulnerability of pigmented nigral neurons in PD and provide a mechanistic framework for the development of future targeted therapeutics.

neuroscience↗

bioRxiv2026

VASP association stabilizes the actin nucleus revealed by molecular dynamics simulations

Actin nucleation is a rate-limiting step in F-actin assembly. However, early actin trimers are intrinsically unstable, with limited inter-subunit contacts rendering them prone to dissociation. VASP is an important regulator of actin polymerization and assembly, but how it stabilizes unstable trimeric actin nucleus remains poorly understood at the molecular level. Here, we use coarse-grained and all-atom molecular dynamics simulations to characterize the interfacial stability and conformational features of VASP-bound actin trimers. Energetic and interfacial analyses show that VASP preferentially anchors the laterally associated subunit through multivalent interactions and reinforces its interfaces with the longitudinal actin dimer. Within the VASP-bound trimer, the three actin subunits retain distinct local conformational features, as reflected by differences in W-loop gating and SD1-to-SD4 dihedral angle. Across replicate simulations, VASP stabilizes the actin nucleus while preserving subunit-specific conformational heterogeneity, rather than uniformly rigidifying the trimer. These findings provide a molecular basis for VASP-mediated stabilization of early actin nuclei and offer broader mechanistic insights into the regulation of protein assembly.

biophysics↗

bioRxiv2026

IsoMatch enables population-scale transcript integration and reveals reference-dependent isoform novelty

Transcriptome analyses increasingly generate large collections of transcript models that must be integrated, compared and interpreted across samples and reference annotations. Transcript novelty depends on structural equivalence criteria and reference annotation. Here we present IsoMatch, a scalable framework that harmonizes transcript merging and reference-based classification through explicit, configurable structural criteria. IsoMatch preserves source-GTF and original-transcript traceability throughout the workflow, allowing every merged and classified model to be traced back to its contributing inputs. Benchmarking showed that IsoMatch preserves distinct transcript models during integration through configurable matching parameters for both splice junction chains and transcript ends. With its index-based architecture, IsoMatch merged 163 ENCODE transcript catalogs and classified 7.9 million transcript models with peak memory usage of 0.49 GB and 1.1 GB, respectively. Integrating GENCODE, RefSeq and CHESS revealed that 71.2% of transcript groups remained annotation-specific even without terminal constraints. For 4.84 million merged transcript models from 119 long-read transcriptomes from the 1000 Genomes Project, the integrated reference increased the full-splice match proportion from 12.0-13.6% with individual annotations to 18.0%. IsoMatch enables scalable transcript integration and classification with explicit structural criteria and preserved traceability, demonstrating how reference choice shapes estimates of transcript novelty.

bioinformatics↗

bioRxiv2026

DiffRhyShiny: A Benchmarking-Informed Platform for Differential Rhythmicity Analysis with Dependence-Aware Evidence Integration

Motivation: Differential rhythmicity analysis identifies condition-specific changes in biological oscillations, but diverse algorithmic assumptions complicate method selection. Furthermore, integrating complementary evidence across different tools while accounting for statistical dependence remains an unresolved challenge. Results: We benchmarked seven algorithms across diverse rhythmic scenarios, revealing that optimal selection depends heavily on specific biological variations and experimental designs. To overcome single-algorithm limitations, we introduced Kost's Method, a dependence-aware p-value integration approach. Building on this framework, we developed DiffRhyShiny, an interactive web platform that automates robust differential rhythmicity analysis, consensus evaluation, and visualization. Furthermore, we validated the platform's performance and statistical rigor using both extensive simulated scenarios and real-world transcriptomic datasets. Availability and implementation: DiffRhyShiny is freely available at https://omicsai.shinyapps.io/diffrhyshiny, with the latest source code at https://github.com/liuchuan111/diffrhyshiny. The code and data to reproduce all benchmarking results are archived on Figshare at https://doi.org/10.6084/m9.figshare.33070100.

bioinformatics↗

bioRxiv2026

Astrocyte-Specific dsRNA-ADAR1 Signalling Links Innate Immunity to Complement Activation in Parkinson Disease

Background: Dysregulated innate immune activation is a key feature of Parkinson disease (PD), but the upstream triggers and cell-type-specific mechanisms remain unclear. Double-stranded RNA (dsRNA) has emerged as a potential driver of innate immune responses. Adenosine deaminase acting on RNA 1 (ADAR1), particularly its interferon-inducible p150 isoform, edits dsRNA to limit aberrant immune activation. Whether this dsRNA-ADAR1 axis contributes to neuroinflammation in PD in a cell-type-specific manner is unknown. Methods: We performed immunofluorescence on post-mortem cingulate cortex tissue from PD cases and controls to localise dsRNA, ADAR1 p150 and pathological alpha-synuclein protein. We integrated these findings with single-nucleus RNA sequencing data from three independent human brain datasets. An eight-module gene scoring framework was applied to assess dsRNA processing, sensing, and downstream innate immune pathways in neurons and astrocytes. Results: In PD cortex, the proportion of dsRNA-positive astrocytes was higher than in controls, whereas neuronal dsRNA positivity did not differ between groups. ADAR1 p150 positivity increased in both astrocytes and neurons, and the proportion of ADAR1 p150 positive astrocytes correlated with astrocytic -synuclein burden. Transcriptomic analyses showed that neurons were enriched for dsRNA editing and processing machinery, whereas astrocytes exhibited higher expression of dsRNA sensing pathways, innate immune responses, and transposable element control. While overall pathway activity did not differ between PD and controls after covariate adjustment, network analyses revealed cell-type-specific coupling patterns. Notably, complement gene expression showed a strong and selective association with dsRNA-related pathways in astrocytes, whereas interferon and NF-{kappa}B signalling were shared across cell types. In both cell types, ADAR1-associated gene programmes were enriched for oxidative phosphorylation and PD-related pathways. Conclusions: These findings identify astrocytes as a central site of relative dsRNA accumulation and ADAR1 p150 induction in the PD cortex. The selective coupling of dsRNA sensing to complement activation in astrocytes highlights a cell-type-specific innate immune pathway that is constitutively present rather than induced by disease. Together, our protein-level and network-level findings converge on a coherent and testable model in which a constitutive astrocytic circuit is engaged by disease-associated dsRNA accumulation, with potential relevance for disease progression and therapeutic targeting.

neuroscience↗

bioRxiv2026

Robust V(D)J Recombination in the Absence of SA1/2-mediated Chromatin Loops

The cohesin ring complex organizes and mediates functional activity of the genome through chromatin loop extrusion1,2. Cohesin comprises a core SMC1-SMC3-RAD21 ring associated with multiple regulatory proteins, including SA1 and SA2 paralogs that bind mutually-exclusively to a RAD21 site3,4. Functional studies of cohesin complexes in vivo often employ inactivation of particular components2,3. However, inactivation of SA1 or SA2 in mice is limited by mouse embryonic lethality5-7 and, for dual SA1/2-inactivation by cycling cell lethality8-10. For these reasons, it remains unknown whether the requirement for SA1/2 in loop extrusion in vitro11,12 applies in vivo1,2, and whether these factors have overlapping or distinct functions in regulating this process3. Here, we employ G0/G1-arrested v-Abl pro-B lines, which are viable in the absence of cohesin-complex factors13,14, to dissect SA1 and SA2 functions in V(D)J recombination, a strictly G0/G1-specific process that harnesses loop extrusion to effect distinct Igh and Ig{kappa} locus recombination mechanisms15,16. Robust long-range Igh or Ig{kappa} V(D)J recombination continued unabated in the absence of SA1 or SA2. However, inactivation of both abrogated most positioned chromatin loops genome-wide and also abolished long-range Igh V(D)J recombination with a phenotype similar to RAD21 (cohesin) inactivation13. Yet, robust Ig{kappa} V(D)J recombination continued upon dual SA1/2-inactivation, which along with Hi-C findings, demonstrates ongoing loop-extrusion in the absence of SA1/2. Correspondingly, replacement of weak Igh V(D)J recombination signal sequences (RSSs) that target V(D)J recombination activity, with strong Ig{kappa} counterparts15 restored long-range Igh V(D)J recombination in SA1/2 inactivated cells. We conclude that SA1/2 together maintain loop extrusion impediments and genome-wide architecture in G0/G1-arrested cells; and, in their absence, loop extrusion proceeds unimpeded. Remarkably, strong RSSs allow Ig{kappa} to function normally in the absence of internal positioned loop structure across the 3.1-mega-base Ig{kappa} locus. We discuss implications of our findings for long-range gene regulation in G0/G1-arrested cells more broadly.

immunology↗

bioRxiv2026

YGEP: An Integrated Web Platform for Genomic Evaluation and Intelligent Breeding in Yak

Background: The yak (Bos grunniens) is an essential livestock species in high-altitude alpine ecosystems, serving key roles in meat, milk, and wool production as well as ecological maintenance. However, traditional yak breeding systems face several limitations, including fragmented herd data management, inefficient phenotypic collection, missing pedigree records, and high technical barriers to applying genomic technologies. These factors constrain the widespread application of modern genetic evaluation methods in yak breeding programs. Description: We developed the Yak Genomic Evaluation Platform (YGEP, https://yakbreeding.swun.edu.cn/en/), an integrated digital system for yak genetic selection and herd management. YGEP integrates three core analytical modules: pedigree reconstruction, genotype imputation and genomic breeding value estimation. Benchmarking on 706 yaks showed that GBLUP achieved a prediction accuracy of r = 0.96 for cannon bone circumference (CCB), and KBeagle reached 84.0% imputation accuracy with the combined reference panel. Conclusion: YGEP bridges the gap between advanced genomic analytics and practical yak breeding. By integrating pedigree reconstruction, genotype imputation, and GEBV estimation into a user-friendly cloud platform, YGEP effectively eliminates technical and data bottlenecks. Overall, it provides an accessible, scalable, and standardized solution to advance digital herd management and precision genetic improvement in alpine livestock.

bioinformatics↗

bioRxiv2026

Dendritic Morphology Constrains Microtubule Dynamics and Organization

Neuronal dendrites form highly branched morphologies supported by spatially patterned microtubule arrays that are essential for intracellular transport and structural stability, yet how this organization is established and maintained remains unclear. Here we analyze microtubule behavior in Drosophila sensory neurons using live-cell imaging, deep learning-based analysis, and genetic perturbations. We find that microtubules are shorter and fewer in thin distal branches and progressively longer and more abundant in thicker proximal branches. Locally, polymerization slows in narrow branches due to soluble tubulin depletion, while catastrophe frequency increases near branch points upon cortical encounter. A physical model incorporating these morphology-dependent constraints reproduces both local microtubule dynamics and global organization. Inverting the textbook view that microtubule dynamics shape cell morphology, our results demonstrate that, on short timescales, cell morphology instead constrains microtubule dynamics, thereby determining large-scale cytoskeletal organization.

biophysics↗

bioRxiv2026

Programmable sequence-resolved in situ profiling of glycoRNA-protein interactomes by GlycoRNA-Map

Cell-surface glycoRNAs participate in diverse biological processes, but they were investigated as pooled populations and the protein interactome of individual glycoRNA species remain unresolved. Here we develop GlycoRNA-Map, a programmable photocatalytic proximity-labeling method that maps proteins surrounding sequence-defined endogenous glycoRNAs on living cells. A sialic-acid-binding aptamer and an RNA-hybridization probe jointly recruit a fluorophore-modified oligonucleotide through dual recognition of the glycan and RNA moieties of glycoRNAs. Light-activated biotinylation followed by quantitative mass spectrometry identified 200-418 enriched proteins for each of five glycoRNAs in A549 cells. Comparison of these proteomes revealed limited overlap among different glycoRNA sequences and across cell types, demonstrating that glycoRNA-associated protein environments depend on both RNA identity and cellular context. Transforming growth factor-{beta} receptors 1 and 2 (TGFBR1 and TGFBR2) were identified in the proximal proteomes of multiple glycoRNAs. Removal of cell-surface RNAs enhanced TGF-{beta} receptor signaling and promoted cell migration, demonstrating that glycoRNAs can functionally regulate extracellular signaling by interacting with their protein neighborhoods. GlycoRNA-Map thus provides an accessible strategy for resolving sequence-specific glycoRNA-protein interactome and discovering their roles in membrane signaling.

biochemistry↗

bioRxiv2026

Mechanism of molecular recognition revealed through dynamic drug binding pathways to SARS-CoV-2 main protease

Characterization of drug-binding pathways remains experimentally limited by transient intermediates and computationally challenging due to long timescales intractable for conventional molecular dynamics. To address these challenges, we combined solution NMR titrations with weighted ensemble (WE) enhanced sampling simulations to resolve atomistic pathways of nirmatrelvir binding to the SARS-CoV-2 main protease. NMR titration revealed residue-dependent heterogeneity spanning fast, intermediate, and slow exchange regimes. WE simulations complement the NMR by providing insights into unassigned residues and adding time-resolved and three-dimensional structural context. We map key interactions along two distinct binding pathways, provide dynamic explanations for residues involved in resistance, and capture unique backbone conformations compared to those sampled in unbound or bound states. Our comprehensive binding model is consistent with a combined conformational selection and induced fit mechanism in which early transient contacts are made with residues E47 and L50 and allosteric motions are centered around residue V204 of the distal domain. This synergistic application of WE and titration NMR enables a more comprehensive characterization of drug binding than either method alone, providing an integrated framework that may have broader applicability to defining structure-kinetic relationships and guiding design of next-generation inhibitors.

biophysics↗

bioRxiv2026

Chemical Descriptors and Deep Learning Embeddings for Scoring de novo Peptide Designs

Peptides occupy a valuable niche between small molecules and biologics, but the clinical translation of de novo peptide designs requires rigorous scoring to simultaneously optimise target binding affinity alongside multiple developability traits, including stability, membrane permeability, aggregation propensity, and non-fouling behaviour. Here, we evaluate two distinct approaches for scoring these candidates: classical chemical descriptors and modern deep learning representations derived from protein language and folding models. Assembling nine public datasets spanning five developability traits and four binding-affinity endpoints, we find sequence-derived chemical descriptors alone contain sufficient information to predict developability task labels effectively. Given their drastically lower computational cost and higher interpretability, classical machine learning models trained on these simple descriptors frequently match or approach the performance of complex deep learning architectures, emerging as a highly efficient and interpretable alternative for high-throughput scoring. Finally, for scoring binding affinity, we demonstrate that Boltz-2 pair representations capture the most information among the tested representations; however, the model's predictive power is confounded by a significant bias from the molecular weight of the peptides. Together, these results establish a comprehensive assessment of state-of-the-art methods for predicting both peptide developability and binding affinity, highlighting the enduring value of interpretable chemical descriptors alongside deep learning in the scoring and selection of de novo peptide designs.

bioinformatics↗

bioRxiv2026

A Multi-Omics Study of Cognitive Resilience to Alzheimer's Disease

Alzheimers disease (AD) is the most severe form of dementia. While significant efforts have been made to identify AD risk factors and develop therapeutics, much less attention has been given to neurobiological factors and molecular signatures associated with cognitive resilience. Substantial evidence suggests that modifiable factors, such as physical and mental activity, may contribute to cognitive reserve and resilience. To this end, we developed a resilient cohort of paired genetics, transcriptomics and proteomics data from the prefrontal cortex of 282 cognitively normal older adults. We conducted extensive preprocessing and rigorous quality control (QC), including QC on sequence metrics and sample matching across multiomics. This cohort can be leveraged for identification of molecular signatures of cognitive resilience to AD and is freely shared with the research community.

genomics↗

bioRxiv2026

Dissecting the TMEM132A-EGFR Dependency to Unlock Translational Therapeutic Opportunities for Pan-Solid Tumor

Solid tumors remain refractory to conventional treatments, yet cell surface proteins, by virtue of their extracellular accessibility and critical roles in tumor signaling, represent an attractive class of targets for precision-targeted therapy. Here, we report that TMEM132A is an essential and previously unrecognized pan-cancer target. TMEM132A interacts directly with EGFR and stabilizes its expression, thereby tethering EGFR at the plasma membrane and sustaining constitutive activation of lipid synthesis. Mechanistically, the TMEM132A-EGFR axis promotes lipogenesis by facilitating SREBP nuclear translocation, which in turn upregulates ACLY and ACSS2 expression to drive acetyl-CoA production and downstream lipid biosynthesis, ultimately disrupting lipid droplet homeostasis. To therapeutically target this axis, we developed a nanobody, LFNanoT132A#3, which effectively blocks the TMEM132A-EGFR interaction, abrogates downstream signaling activation, and potently inhibits proliferation across multiple solid tumor types. Notably, LFNanoT132A also exerts robust antitumor activity against H1975 xenografts, a model resistant to first- and second- generation EGFR inhibitors, underscoring its potential to overcome conventional drug resistance. Our findings establish TMEM132A#3 as a critical node in membrane-tethered oncogenic signaling and metabolic rewiring, and position LFNanoT132A#3 as a promising therapeutic candidate for precision cancer therapy.

cancer biology↗

bioRxiv2026

SALRR: Scalable Analysis of Long-Read RNA-Seq Enables Comprehensive Transcriptome Profiling in Human Brain

Isoform-resolved transcriptomics is fundamental to decoding the molecular complexity of the human brain, yet population-scale long-read RNA sequencing has remained inaccessible due to labor-intensive library preparation, sensitivity to RNA degradation in postmortem tissue, and the absence of integrated, reproducible analysis pipelines. Here we present SALRR (Scalable Analysis of Long-Read RNA-seq), an integrated wet-lab and computational platform designed to overcome these barriers. Automated ONT long-read cDNA library preparation on the Hamilton Microlab NGS STAR platform reduces hands-on time by 67% and enables 24 libraries per operator per day while maintaining performance across RNA integrity values. A modular, Snakemake-based pipeline performs end-to-end processing from ONT signal data to isoform-level quantification, incorporating SIRV spike-in calibration, multi-stage quality control, and stringent isoform validation. Applied to 10 postmortem frontal cortex samples from the North American Brain Expression Consortium, SALRR identified 31,607 high-confidence isoforms from 10,075 genes, including 8,532 novel splice variants absent from GENCODE v49, and complex splicing events systematically missed by short-read sequencing at neurodegeneration-relevant loci, including GBA1, CCNF, CHCHD10, and TREM2. All protocols and code are openly available, providing a scalable, community-ready framework for isoform-resolved transcriptomics in neurodegeneration, aging, and complex brain disease.

genomics↗

bioRxiv2026

scLANTERN: High-Throughput Retrospective Lineage Tracing via Full-Length Single-Cell Transcriptomics and Expressed Repeat Variation

Understanding the lineage relationships among individual cells is a key pursuit of modern biology, essential for unraveling the complexities of developmental processes and the adaptive mechanisms of disease progression, particularly in oncology. Retrospective single-cell clonal tracing has emerged as a transformative approach, offering a unique window into the evolutionary trajectories of cancer within clinical samples. While short-read single-cell transcriptomics (scRNA-seq) has revolutionized our ability to map cell states across human tumor atlases, it remains fundamentally limited in its capacity to link these states with high-resolution genomic alterations and the evolutionary trajectories inferred from these natural variants. Integrating somatic mutation discovery with transcriptomic profiles at single-cell resolution often requires separate, costly, and low-throughput genomic assays. Furthermore, existing methods frequently rely on exogenous genetic labeling or are restricted to short-read sequencing, which typically fails to resolve complex genomic rearrangements, large indels, or variations within highly repetitive regions,such as short tandem repeats (STRs), that could serve as potent endogenous clonal markers.

evolutionary biology↗

bioRxiv2026

Design and Assembly of Combinatorial DNA Barcodes for Probe-based Genomics Applications

Probe-based genomics technologies are extending molecular analysis into intact tissues and fixed cells, yet strategies to decode complex experimental conditions encoded in cellular RNA remain limited. Here we present a modular framework that integrates custom software tools with purpose-built cloning reagents to design, assemble, validate, and deploy combinatorial DNA barcodes. Combinatorial barcodes comprise spatially adjacent collections of known sequences, enabling millions of unique molecules to be efficiently distinguished using a limited set of probes. Our software tools integrate with optimized assembly plasmids and whole plasmid long-read sequencing for high-fidelity construction and structural validation of diverse combinatorial barcode architectures. Assembled barcode libraries are flexibly transferred into user-modified expression vectors to support diverse downstream experimental applications. We showcase the versatility of this framework by assembling two structurally distinct combinatorial barcode libraries, each containing millions of unique sequences. Following rabies virus-based delivery to the mouse brain, we validate in vivo decoding of a combinatorial barcode architecture capable of distinguishing ~16.3 million expressed RNAs through probe-based in situ sequencing. Our framework for flexible and accurate combinatorial barcode construction fills a technically demanding niche delivering cost-effective molecular reagents for multiplexed experimentation on current and evolving probe-based genomics platforms.

genomics↗