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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Lipid Headgroup Hydration Regulates Distinct Remodeling of the Membrane Interface by Polyethylene Glycol and Dextran

Water-soluble polymers commonly interact with cell membranes, but their interactions are poorly understood. Here, we investigate polyethylene glycol (PEG) and dextran (DEX) interactions with different model lipid membranes. Using total internal reflection fluorescence microscopy, we observe that PEG and DEX trigger strikingly different membrane responses - DEX induces extensive membrane remodeling, including localized multilamellar domain formation, while PEG does not. Combining fluorescence spectroscopy, fluorescence anisotropy, and vibrational sum frequency spectroscopy, we show that DEX perturbs lipid headgroup hydration by displacing interfacial water with minimal effects on lipid packing, while PEG largely preserves this hydration layer. We find that membrane binding affinity alone does not determine the extent to which hydrophilic polymers perturb membrane structure and interfacial properties; and that lipid headgroup hydration, rather than lipid charge, is a general regulator of hydrophilic polymer-membrane interactions. This work gives mechanistic insights into how neutral polymers interact with cells and vesicles, with relevance to cell biology and drug delivery. O_FIG O_LINKSMALLFIG WIDTH=169 HEIGHT=200 SRC="FIGDIR/small/739366v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@16f7374org.highwire.dtl.DTLVardef@4e94forg.highwire.dtl.DTLVardef@7175dcorg.highwire.dtl.DTLVardef@f0e5b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Coevolution of Codependent Hosts and Symbionts

Many endosymbioses in eukaryotes superficially appear to be beneficial to both participants. However, there is little direct evidence for this, and symbioses naturally set up conditions in which each member of the pair is under selection to extract resources from the other. Ultimately, the endosymbiont either evolves to be in conflict with the interests of the host or to act cooperatively with the host contrary to its own best interests. Focusing on obligate symbioses, we develop theory to clarify the population-genetic conditions favoring the alternative outcomes. The balance is usually tipped in favor of exploitation by the symbiont, particularly when the number of symbionts within host cells is high, selection is strong on symbionts relative to hosts, there is horizontal transfer of symbionts, and/or the symbionts have accelerated mutation rates or turnover times. If the symbiont conditions the host-cell biology to enhance within-host population sizes, selection for selfish symbionts will be further enhanced by the diminished level of within-host drift. Although the host evolves in parallel to exploit resources from the endosymbiont, the net result is often a stalemate in which the host is no better off than prior to host-symbiont coevolution. Strict vertical inheritance can result in an evolutionary alignment of interests of the endosymbiont and the host, as this minimizes the possibility of within-host selection, but even here there is a critical host population size below which the symbiont evolves to exploit the host. These results suggest that the evolutionary enslavement of a symbiont to benefit a host species requires a narrow mix of population-biological features of both participants.

evolutionary biology↗

bioRxiv2026

Multiple Particle Tracking via Velocity Filtering (MPT-vVF): a velocity filtering framework for robust tracking moving organelles in living cells

Living cells are highly dynamic and densely crowded environments in which organelles such as vesicles undergo continuous motion that is essential for cellular processes. Therefore, accurate tracking of individual organelles is crucial for understanding intercellular dynamics and functions. However, precise tracking of individual organelles in living cells remains challenging due to high organelle densities, frequent particle overlap, and the coexistence of stationary and motile organelles. In particular, stationary organelles can obscure the trajectories of moving organelles, leading to tracking errors and fragmented tracks. To overcome these challenges, we developed Multiple Particle Tracking via Velocity Filtering (MPT-vVF), an unbiased, semi-automated tracking framework that incorporates a mathematically derived velocity-filtering algorithm to selectively identify and track moving organelles with high accuracy in crowded intracellular environments. MPT-vVF integrates denoising, background subtraction, and a velocity-matching detection step that discriminates true particle motion from noise based on spatiotemporal continuity, followed by robust trajectory linking. We demonstrate that MPT-vVF can accurately resolve nanometer-scale displacements of immobilized beads, highlighting its high tracking precision. We also validate the robustness of MPT-vVF by quantifying the transport of brain-derived neurotrophic factor (BDNF)-mRFP-containing vesicles in living hippocampal neurons. Furthermore, MPT-vVF reveals that exposure to 50-nm nanoplastics impairs vesicular transport, reducing both travel length and speed of BDNF-containing vesicles in living neurons. These findings establish MPT-vVF as a powerful method for quantitative analysis of intracellular organelles in crowded living cells and suggest its broad application to biophysics, cell biology, and soft matter research.

biophysics↗

bioRxiv2026

TEAD1 signaling modulates adrenal chromaffin maturation

Chromaffin cells synthesize and secrete catecholamines to coordinate systemic stress responses and regulate diverse neuroendocrine and metabolic functions. However, the molecular mechanisms governing chromaffin-cell differentiation and their disruption in pheochromocytoma (PC) remain incompletely understood. Here, through integrated analyses of human developmental atlases, patient-derived transcriptomic datasets, genetically engineered mouse models, and chromaffin organoids, we identify TEAD1 signaling as a critical regulator of chromaffin-cell differentiation and function. In vivo studies using a chromaffin cell-specific TEAD1 overexpression mouse model demonstrated that suppression of TEAD signaling markedly compromises chromaffin-cell differentiation and endocrine function. Additionally, compared with other TEAD family members, TEAD1 transcriptional activities are readily affected by sequences near the binding motif. To identify therapeutically actionable regulators of TEAD1 signaling, we established a TEAD activity-based screening platform and identified the serotonin receptor HTR5A antagonist SB699551 as a potent modulator of chromaffin-cell state. SB699551 suppressed PC-cell proliferation in vivo, and remodeled catecholamines synthesis in primary human PC cells. Additionally, application of SB699551 to human PC tumor revealed a subpopulation of primary chromaffin cells sensitive to this compound. Mechanistically, CXXC5 and L1CAM were identified as downstream SB699551-TEAD1 signaling effectors mediating chromaffin-cell proliferation and differentiation. Overall, we demonstrate that TEAD1 signaling is a fundamental mechanism regulating chromaffin cell differentiation and that modulation of TEAD1 signaling via SB699551 offers a new area of investigation in chromaffin cell biology.

molecular biology↗

bioRxiv2026

NOTCH3 Modulation of Extracellular Matrix, Cytoskeletal Organisation and Metabolic Functions in Human Vascular Smooth Muscle Cells

NOTCH3 is a transmembrane receptor highly expressed in vascular mural cells where it contributes to blood vessel formation and homeostasis. NOTCH3 expression declines in the vasculature with aging, and dysregulated NOTCH3 signalling is implicated in pulmonary arterial hypertension, cancer progression and CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy). RNA-based approaches targeting NOTCH3 are emerging as potential therapeutic strategies, however, the consequences of NOTCH3 suppression in mature vascular smooth muscle cells (VSMCs) remain incompletely understood. Here, we investigated the molecular and functional effects of siRNA-mediated NOTCH3 knockdown in human aortic smooth muscle cells. Transfection with NOTCH3-targeting siRNA efficiently suppressed NOTCH3 transcript and protein levels. Quantitative proteomics revealed remodelling of extracellular matrix (ECM), cytoskeletal and metabolic pathways, with enrichment of collagen biosynthesis and inhibition of glycolytic signalling. Specifically, NOTCH3 knockdown increased ECM components, including COL3A1, elevated F-actin, and upregulated the actin regulator, CTTN. In parallel, glycolytic capacity was reduced, accompanied by decreased expression of the glycolytic enzyme ENO2. Despite reduced VEGFA and alteration in angiogenic signalling proteins, endothelial network formation in co-cultures, as well as VSMC proliferation and migration remained unaffected. Finally, NOTCH3 interactome analysis revealed key collagen and actin-regulating proteins. These findings identify NOTCH3 as an important regulator of ECM homeostasis, cytoskeletal organisation, and glycolytic metabolism. The preservation of primary cellular functions despite molecular remodelling highlights the adaptive capacity of VSMCs. These findings demonstrate that therapeutic modulation of NOTCH3 may alter vascular cell biology which warrants consideration during development of RNA-based therapeutics for CADASIL and other NOTCH3-associated diseases.

molecular biology↗

bioRxiv2026

Improved ancestral genome reconstruction using a learned gene-content grammar

Ancestral gene content inferences allow inferring the set of genes - and by extension, the cellular features and metabolic capabilities - of ancestral organisms, based on data from modern genomes. Current methods differ in their approach to ancestral inferences and the kinds of errors they make: reconciliation methods map gene trees onto species trees, and tend to under-estimate ancestral contents due to phylogenetic noise; profile methods model the evolution of phylogenetic profiles (presence-absence or count data) on the species tree, and tend to return inflated ancestors because they ignore gene trees and as a result can only account for horizontal gene transfer (HGT) in a limited manner. For reasons of tractability, both approaches also share a core limitation: neither uses the fact that genes do not act alone but belong to operons, protein complexes, and metabolic pathways that may be gained and lost together or experience shared selective constraints. Here, we show that this context - the grammar of gene content - provides a rich source of information that can be used to greatly improve ancestral gene content inference and metabolic reconstruction under both the reconciliation- and profile-based approaches. We model this structure as an Ising model and infer its parameters from 113,104 bacterial and archaeal genomes (one per species representative in GTDB). We validate the model on extant taxa using phylum-level holdout (i.e. using test data from different prokaryotic phyla than training data), showing that it can accurately "denoise", i.e., reconstruct gene repertoires from highly fragmented and noisy input data, learning about protein-protein interactions and gene essentiality during the training process. When applied to ancestral reconstructions, the denoiser fills gaps in conservative reconstructions and removes excess genes from overly-generous ones, such that different reconstruction methods converge to broadly concordant conclusions. By using this gene content grammar, patchy method-dependent ancestral reconstructions can be turned into organism-like ones, and yield agreement on the gene families, cell-biological features, and metabolic capabilities of the deepest nodes in the tree of life.

evolutionary biology↗

bioRxiv2026

Blockiness as Determinant of Liquid Phase Formation by Polypeptide-Like Polymers in Stickers-Spacers Paradigm

Liquid-liquid phase separation (LLPS) driven by intrinsically disordered protein regions underlies the formation of membraneless organelles. The role of these structures in cell biology is an active area of research, yet their physical understanding remains less developed than that of protein assemblies with well-defined, rigid tertiary structures. The amino acid composition of disordered regions, particularly the abundance of charged residues, is known to modulate phase behavior. These factors govern accessible conformations, the assembly of compact sticker clusters, and the porosity of the resulting polymer networks. In contrast, the spatial distribution of side-chain volumes remains a comparatively understudied sequence determinant. Here, we employ a model that captures the patterning of side-chain volumes and interaction strengths without specifying chemical identity to show how sequence blockiness modulates the phase-nucleation propensity, connectivity, and compactness of polypeptide-like polymer assemblies. Using a library of polymers with identical composition but permuted residue arrangements, we show that clustering of bulky residues generates cooperative sticker domains and identify the threshold blockiness required to stabilize polymer networks. Coarse-grained simulations and mean-field theory reveal that this effect arises from an interplay among steric repulsion, solvent entropy, and higher-valency interactions, whereby blocky patterns promote the formation of multi-node percolated networks. Segregation of stickers and spacers is therefore one of the sequence parameters governing LLPS which may be useful for interpreting the evolution of disordered protein sequences, high-throughput proteome screening, and engineering of stimuli-responsive biomaterials. These rules may be exploited to predetermine condensate stability, viscoelasticity, and molecular exchange rates without altering chemical functionality.

biophysics↗

bioRxiv2026

SorCS2 controls Wnt signaling homeostasis during neurodevelopment by regulating Ror2 activity and trafficking

Wnt signaling orchestrates key developmental processes, yet mechanisms restricting its spatial and temporal activity remain incompletely understood. Here we identify SorCS2, a VPS10p-domain sorting receptor, as a conserved regulator of Wnt signaling homeostasis. Integrating proteomics, cell biology, embryology, and vertebrate models, we show that SorCS2 interacts with the noncanonical Wnt receptor Ror2 and controls its trafficking, Wnt5a-induced internalization, and lysosomal degradation. In mouse dopaminergic progenitors, SorCS2 deficiency enhances responsiveness to canonical and noncanonical Wnt ligands, increases Ror2 phosphorylation, and amplifies downstream signaling of both Wnt signaling cascades. Loss of SorCS2 further disrupts the ligand-receptor feedback loop, leading to elevated Ror2, Wnt5a, and Wnt1 expression, accelerated proliferation and neurogenesis, and increased expression of Tcf4, a gene implicated in neuropsychiatric disorders. Across vertebrate systems, SorCS2-dependent control of Ror2 signaling is required for anterior-posterior axis elongation, convergent extension, somitogenesis, and brain morphogenesis. These findings define a SorCS2-dependent sorting mechanism that fine-tunes morphogen receptor signaling during vertebrate neurodevelopment.

developmental biology↗

bioRxiv2026

What is pathogenicity? Structural vulnerabilities lead to distinct phenotypes in a membrane protein, ATP1A3

Distinct protein features of pathogenic variants are illustrated here for a gene with an extraordinarily wide range of neurological syndromes: ATP1A3, the neuronal Na,K-ATPase. Vulnerable structure features produce mechanistically different consequences, from loss or gain of function to maladaptive cell biological responses. Variants in specific structural elements (glycines, prolines, and {beta}-sheets) link severe phenotype to impaired folding during biosynthesis. In contrast, impaired protein-protein interactions may result in brain malformation. Defective RNA transcripts, on the other hand, exhibit a range of benign to severe phenotypes, likely due to efficacy variability in the defensive degradation of aberrant mRNA and protein. Variant interpretation will benefit from understanding structure-specific vulnerabilities.

neuroscience↗

bioRxiv2026

Unilateral gene expression via maternally controlled zygotic recombination in Drosophila

Structural and functional asymmetries are ubiquitous in bilaterians, but tools for unilateral genetic access have been lacking, a methodological gap that has limited mechanistic dissections of lateralized cellular functions. We developed the Maternally Controlled Zygotic Recombination (MCZR) system to overcome this limitation in Drosophila, co-opting the regulatory machinery of the maternal-to-zygotic transition to enable somatic recombination during early embryonic development. Targeting recombinase activity to this early window generates large somatic mosaics that frequently split across the anatomical midline. In conjunction with cell type-specific driver lines, this approach provides a reliable, scalable, and user-friendly platform for unilateral transgene expression. In proof-of-concept experiments, we apply our tool to the question of locomotor control, illustrating how MCZR can be used to unilaterally manipulate or monitor a cell type of interest. Overall, the MCZR system unlocks previously inaccessible experimental avenues across neuroscience, cell biology, and developmental biology.

neuroscience↗

bioRxiv2026

Capturing domain-level detail of molecular interactions in the IMEx Consortium binding domain data set

The machinery of the cell depends on physical interactions, between proteins themselves, and between proteins and other biomolecules. The detailed characterisation of these interactions, including the identification of the precise sequence regions mediating binding events, is essential to understand both normal cell biology and disease mechanisms. Protein binding domains are specific regions of a protein sequence that mediate direct contact with a partner and represent a critical layer of functional annotation, bridging sequence, structure, and cellular function. Here we present the IMEx binding domain dataset, a large-scale, manually curated open-access set of molecular interactions assembled over more than two decades by the International Molecular Exchange (IMEx) Consortium. The dataset compiles experimentally derived binding region annotations from peer-reviewed literature, systematically linked to protein-protein interaction (PPI) records and mapped to standardised protein sequences in UniProtKB. We describe the curation model, data format, and content of the dataset, provide a descriptive and comparative analysis, and demonstrate the utility of the dataset as a gold standard for benchmarking interface prediction and variant effect annotation methods. Its integration with genomic variations further offers a route to elucidate the mechanistic basis of disease-associated mutations. The datasets are freely available through IntAct in the dedicated PSI-MI FeatureTAB format at https://ftp.ebi.ac.uk/pub/databases/intact/current/psimitab/features/ and are updated and enhanced with every release.

bioinformatics↗

bioRxiv2026

Inferring protein-induced spontaneous curvature of lipid bilayers from molecular simulation

Proteins and lipids of a biological cell work together to reshape the membranes in which they reside. Membrane reshaping is necessary for vesicular traffic and maintenance of organelle shape and structure. Curvature is the fundamental mathematical description of the local shape of a surface. Through the well-established Helfrich/Canham/Evans model of bilayer shape, curvature sensing and induction are both consequences of a protein's spontaneous curvature, the local curvature at which the Helfrich model energy is a minimum. This paper describes a straightforward approach for estimating the strength and sign of a bilayer-associated molecule's curvature induction from molecular simulation: Correlating the molecule's lateral position with curvature on thermally-fluctuating bilayer modes. Example demonstrations range from a large multi-pass coarse-grained transmembrane protein to an all-atom amphipathic helical segment. Quantification of these systems is placed in the context of experiments seeking the same strength and sign of curvature induction.

biophysics↗

bioRxiv2026

Molecular machines measure the nanomechanics of biological membranes

Mechanical properties such as elastic resistance to distortion define many materials and can routinely be measured with an indenting tip as simple as a finger. We have miniaturized the indentation principle by 8 orders of magnitude to analyze lipid bilayer nanomechanics, which are of relevance in biology and disease progression. Our nanoscale indenting tip is designed with DNA nanotechnology to be an integral part of a molecular machine which autonomously binds to membranes, actively indents localized membrane patches, measures the membranes elastic response to indentation, and converts the indentation degree into an optical readout signal. Compared to serially operated macro-to-microscale indentation probes, our miniaturization strategy enables deploying millions of the indenting machines for parallel analysis of membrane elasticity. The nanoscale molecular machines are also taken up by biological cells to reveal softer inner membrane compartments compared to stiffer cytoplasmic membranes, using indentation depths of solely up to 1 nm. We envision the DNA-based nanotools to contribute to the analysis of membrane mechanics of cells as well as soft polymeric interphases.

biophysics↗

bioRxiv2026

Connectome structure constrains neural activity across scales

Connectomes can be represented at many resolutions, but coarse-graining risks discarding structure relevant to neural dynamics. Here we ask at what resolution a connectome preserves what it predicts about neural activity. In C. elegans, direct synaptic connectivity poorly predicts whether stimulating one neuron evokes a response in another. A parameter-free predictor based on paths through the connectome performs substantially better, with most predictive information carried by two-step paths and by the arrangement rather than the number of connections. Coarse-graining neurons into biological cell classes preserves these predictions despite discarding path structure, whereas random partitions degrade them. Connectivity alone identifies alternative coarse-grainings that preserve prediction down to about nine groups corresponding to nervous-system subsystems. Partitions inferred from newly hatched animals preserve adult predictions, and lineage-based partitions in Drosophila preserve path structure better than random coarse-graining. Functional prediction can therefore survive at resolutions far coarser than those required to preserve structure.

neuroscience↗

bioRxiv2026

Live Droplet Single-Cell RNA Sequencing

Single-cell RNA sequencing has emerged as one of the most influential methods of the past decade, enabling the dissection of cellular heterogeneity and the discovery of rare cell types. However, most workflows require cell lysis and provide only terminal snapshots, limiting their ability to capture dynamic cellular trajectories and temporal variations within the same cell. Herein, we present Live Droplet single-cell RNA sequencing (Live D-seq), a technology that enables scalable transcriptome-wide profiling of individual living cells over time. Live D-seq combines longitudinal intracellular sampling of live, barcoded cells with particle-templated emulsification and barcoded hydrogel beads, enabling repeated samples from the same cell lineage to be linked across time points. We showcase Live D-seq's capabilities by (i) demonstrating minimal cross-contamination in cross-species experiments, (ii) precisely mapping cellular barcodes to hydrogel bead barcodes, and (iii) tracking epithelial-to-mesenchymal transition dynamics in A549 cells over time. Altogether, Live D-seq enables repeated, non-destructive measurements of gene expression in the same single cells, offering a framework to study longitudinal single-cell biology in health and disease.

bioengineering↗

bioRxiv2020

A Streptomyces venezuelae Cell-Free Toolkit for Synthetic Biology

Prokaryotic cell-free coupled transcription-translation (TX-TL) systems are emerging as a powerful tool to examine natural product biosynthetic pathways in a test-tube. The key advantages of this approach are the reduced experimental timescales and controlled reaction conditions. In order to realise this potential, specialised cell-free systems in organisms enriched for biosynthetic gene clusters, with strong protein production and well-characterised synthetic biology tools, is essential. The Streptomyces genus is a major source of natural products. To study enzymes and pathways from Streptomyces, we originally developed a homologous Streptomyces cell-free system to provide a native protein folding environment, a high G+C (%) tRNA pool and an active background metabolism. However, our initial yields were low (36 g/mL) and showed a high level of batch-to-batch variation. Here, we present an updated high-yield and robust Streptomyces TX-TL protocol, reaching up to yields of 266 g/mL of expressed recombinant protein. To complement this, we rapidly characterise a range of DNA parts with different reporters, express high G+C (%) biosynthetic genes and demonstrate an initial proof of concept for combined transcription, translation and biosynthesis of Streptomyces metabolic pathways in a single one-pot reaction.

synthetic biology↗

bioRxiv2023

Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations

An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We found the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.

bioinformatics↗

bioRxiv2025

DISCO-seq: 3D single-cell transcriptomics of intact biological systems

Single-cell transcriptomics has transformed tissue analysis, yet current methods struggle to integrate whole-tissue 3D architecture. Conventional techniques restrict molecular profiling to pre-selected 2D sections, losing systemic context and introducing anatomical bias by sampling less than 0.001% of a whole organism. To overcome these challenges, we developed DISCO-seq, a tissue-clearing chemistry that enables superior RNA accessibility compared to fresh or fixed tissues. DISCO-seq integrates whole-organ or organism 3D imaging with both untargeted and targeted transcriptomics, yielding high-quality RNA from cleared tissues comparable to standard samples. We demonstrate its versatility by investigating tumor heterogeneity in a syngeneic glioblastoma mouse model, using 3D imaging to identify spatially distinct microenvironments and characterize their unique transcriptomic signatures. Moreover, DISCO-seq enabled unbiased, whole-body mapping of SARS-CoV-2 S1 protein deposition in mice, followed by transcriptomic profiling of spatially defined niches. By bridgingmesoscale 3D imaging with single-cell transcriptomics, DISCO-seq establishes a paradigm for anatomically contextualized, hypothesis-free tissue interrogation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/693352v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@758ee5org.highwire.dtl.DTLVardef@1f862c6org.highwire.dtl.DTLVardef@1cf3b9org.highwire.dtl.DTLVardef@c5082e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDISCO-seq integrates RNA-preserving tissue-clearing chemistry with whole-organ or organism 3D imaging, enabling anatomically unbiased single-cell transcriptomics. C_LIO_LIDISCO-seq yields RNA quality and transcriptome profiles equivalent to those obtained from matched fresh or fixed tissues. C_LIO_LIDISCO-seq identifies discrete glioblastoma microenvironments and defines their transcriptomic states within the intact brain. C_LIO_LIDISCO-seq enables whole-body mapping of SARS-CoV-2 S1 and uncovers region-specific immune and metabolic responses across anatomical niches. C_LI Supplementary movies can be seen at: http://discotechnologies.org/DISCO-seq/

systems biology↗