Biology subjects

Zeng, H.

Publications and source records attributed to Zeng, H..

At least 109 records · Page 6Linked to original sources

bioRxiv2023

Modeling the cell-type specific mesoscale murine connectome with anterograde tracing experiments

The Allen Mouse Brain Connectivity Atlas (MCA) consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class specific whole brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method which we previously used to fill in spatial gaps to also fill in a gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type and structure specific connectivities. We also show that the wild type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model. AUTHOR SUMMARYLarge-scale studies have described the connections between areas in multiple mammalian models in ever expanding detail. Standard connectivity studies focus on the connection strength between areas. However, when describing functions at a local circuit level, there is an increasing focus on cell types. We have recently described the importance of connection types in the cortico-thalamic system, which allows an unsupervised discovery of its hierarchical organization. In this study we focus on adding a dimension of connection type for a brain-wide mesoscopic connectivity model. Even with our relatively massive dataset, the data in the cell type direction for connectivity is quite sparse, and we had to develop methods to more reliably extrapolate in such directions, and to estimate when such extrapolations are impossible. This allows us to fill in such a connection type specific inter-areal connectivity matrix to the extent our data allows. While analyzing this complex connectivity, we observed that it can be described via a small set of factors. While not complete, this connectivity matrix represents a a categorical and quantitative improvement in mouse mesoscale connectivity models.

neuroscience↗

bioRxiv2023

Brain-wide Correspondence Between Neuronal Epigenomics and Long-Distance Projections

Single-cell genetic and epigenetic analyses parse the brains billions of neurons into thousands of "cell-type" clusters, each residing in different brain structures. Many of these cell types mediate their unique functions by virtue of targeted long-distance axonal projections to allow interactions between specific cell types. Here we have used Epi-Retro-Seq to link single cell epigenomes and associated cell types to their long-distance projections for 33,034 neurons dissected from 32 different source regions projecting to 24 different targets (225 source [->]target combinations) across the whole mouse brain. We highlight uses of this large data set for interrogating both overarching principles relating projection cell types to their transcriptomic and epigenomic properties and for addressing and developing specific hypotheses about cell types and connections as they relate to genetics. We provide an overall synthesis of the data set with 926 statistical comparisons of the discriminability of neurons projecting to each target for every dissected source region. We integrate this dataset into the larger, annotated BICCN cell type atlas composed of millions of neurons to link projection cell types to consensus clusters. Integration with spatial transcriptomic data further assigns projection-enriched clusters to much smaller source regions than afforded by the original dissections. We exemplify these capabilities by presenting in-depth analyses of neurons with identified projections from the hypothalamus, thalamus, hindbrain, amygdala, and midbrain to provide new insights into the properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription factor binding motifs, and neurotransmitter usage.

neuroscience↗

bioRxiv2023

Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain

Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brains 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq1) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH2) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq3 dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brains cellular-spatial and regulatory genome diversity.

genomics↗

bioRxiv2023

Low-dose cytokine immunotherapy of solid cancers enabled by phagocytic-competent protein co-crystals

Protein therapeutics are often compromised by sub-optimal biodistribution contributing to poor efficacy and adverse events. Drug delivery mechanisms better able to target protein drugs to the disease site and provide localized, sustained release have the potential to transform therapeutic standards. PODS(R) crystals (PODS) are natural-mimetic, micron-scale protein co-crystals engineered to incorporate a protein cargo that can be sustainably released under the action of resident proteases. PODS are efficiently taken up by phagocytic cells with the cargo protein subsequently released in a bioactive form. Since blood-circulating phagocytic cells, including monocytes, are actively recruited into diseased and inflamed tissue, such as the tumour microenvironment, we postulated that monocyte/macrophage-mediated PODS delivery could be used as a molecular "Trojan horse" to efficiently deliver therapeutic proteins to target cells. This could improve the pharmacodynamics and pharmacokinetics of protein drug delivery to treat systemic and disseminated diseases. Interleukin-2 (IL-2) is notoriously toxic at the high doses required for therapeutic efficacy. Here, we demonstrate the therapeutic efficacy and tolerability of low doses of PODS containing IL-2 cargo (PODS-IL-2) administered intravenously in a mouse model of melanoma. We further demonstrate the therapeutic benefit of PODS delivering IL-2, interleukin-15 (IL-15) and interferon gamma (IFN-{gamma}) in a mouse model of renal cell carcinoma at two doses. Efficacy was seen in both doses with the higher dose generating rapid and complete rejection of the tumour in some of the mice treated with each cytokine. This study provides proof-of-concept for the utility of intravenously administered PODS to provide a generalised and widely applicable mechanism to effectively deliver protein drugs for the therapy of cancer and potentially other diseases.

cancer biology↗

bioRxiv2023

A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming

This paper explicates a solution to the problem of building correspondences between molecular-scale transcriptomics and tissue-scale atlases. The central model represents spatial transcriptomics as generalized functions encoding molecular position and high-dimensional transcriptomic-based (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling each atlas compartment as a homogeneous random field with unknown transcriptomic feature distribution. The algorithm presented solves simultaneously for the minimizing geodesic diffeomorphism of coordinates and latent atlas transcriptomic feature fractions by alternating LDDMM optimization for coordinate transformations and quadratic programming for the latent transcriptomic variables. We demonstrate the universality of the algorithm in mapping tissue atlases to gene-based and cell-based MERFISH datasets as well as to other tissue scale atlases. The joint estimation of diffeomorphisms and latent feature distributions allows integration of diverse molecular and cellular datasets into a single coordinate system and creates an avenue of comparison amongst atlas ontologies for continued future development.

bioengineering↗

bioRxiv2023

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology 2, 3, electrophysiology 4, 5, transcriptomic expression 6-8, connectivity 9-13, or even a combination of such modalities 14-16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

neuroscience↗

bioRxiv2023

Unsupervised pattern discovery in spatial gene expression atlas reveals mouse brain regions beyond established ontology

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e. staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a new brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis (PCA) and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene co-expression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data that are infeasible by conventional manual approaches.

neuroscience↗

bioRxiv2023

A molecularly defined and spatially resolved cell atlas of the whole mouse brain

In mammalian brains, tens of millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells in the brain have so far hindered our understanding of the molecular and cellular basis of its functions. Recent advances in spatially resolved single-cell transcriptomics have allowed systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1-3. However, these approaches have only been applied to a few brain regions1-11 and a comprehensive cell atlas of the whole brain is still missing. Here, we imaged a panel of >1,100 genes in [~]8 million cells across the entire adult mouse brain using multiplexed error-robust fluorescence in situ hybridization (MERFISH)12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating MERFISH and single-cell RNA-sequencing (scRNA-seq) data. Using this approach, we generated a comprehensive cell atlas of >5,000 transcriptionally distinct cell clusters, belonging to [~]300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of the MERFISH images to the common coordinate framework (CCF) of the mouse brain further allowed systematic quantifications of the cell composition and organization in individual brain regions defined in the CCF. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes in the gene-expression profiles of cells. Finally, this high-resolution spatial map of cells, with a transcriptome-wide expression profile associated with each cell, allowed us to infer cell-type-specific interactions between several hundred pairs of molecularly defined cell types and predict potential molecular (ligand-receptor) basis and functional implications of these cell-cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a valuable resource for future functional investigations of neural circuits and their dysfunction in diseases.

neuroscience↗

bioRxiv2023

P53 Acetylation Exerts Critical Roles In Pressure Overload Induced Coronary Microvascular Dysfunction and Heart Failure

Coronary microvascular dysfunction (CMD) has been shown to contribute to cardiac hypertrophy and heart failure with preserved ejection fraction. At this point, there are no proven treatments for CMD. We have shown that histone acetylation may play a critical role in the regulation of CMD. By using a mouse model that replaces lysine with arginine at residues K98/117/161/162R of p53 (p534KR), preventing acetylation at these sites, we test the hypothesis that acetylation-deficient p534KR could improve coronary microvascular dysfunction and prevent the progression of hypertensive cardiac hypertrophy and heart failure. Wild-type (WT) and p534KR mice were subjected to pressure overload (PO) by transverse aortic constriction to induce cardiac hypertrophy and heart failure (HF). Echocardiography measurements revealed improved cardiac function together with reduction of apoptosis and fibrosis in p534KR mice. Importantly, myocardial capillary density and coronary flow reserve (CFR) were significantly improved in p534KR mice. Moreover, p534KR upregulated the expression of cardiac glycolytic enzymes and glucose transporters, as well as the level of fructose-2,6-biphosphate; increased PFK-1 activity; and attenuated cardiac hypertrophy. These changes were accompanied by increased expression of HIF-1 and proangiogenic growth factors. Additionally, the levels of SERCA-2 were significantly upregulated in sham p534KR mice as well as in p534KR mice after TAC. In vitro, p534KR significantly improved endothelial cell (EC) glycolytic function and mitochondrial respiration, and enhanced EC proliferation and angiogenesis. Similarly, acetylation-deficient p534KR significantly improved CFR and rescued cardiac dysfunction in SIRT3 KO mice. Our data reveal the importance of p53 acetylation in coronary microvascular function, cardiac function, and remodeling, and may provide a promising approach to improve hypertension-induced coronary microvascular dysfunction (CMD) and to prevent the transition of cardiac hypertrophy to heart failure.

pathology↗

bioRxiv2023

A bioinformatics screen reveals Hox and chromatin remodeling factors at the Drosophila histone locus

Cells orchestrate histone biogenesis with strict temporal and quantitative control. To efficiently regulate histone biogenesis, the repetitive Drosophila melanogaster replication-dependent histone genes are arrayed and clustered at a single locus. Regulatory factors concentrate in a nuclear body known as the histone locus body (HLB), which forms around the locus. Historically, HLB factors are largely discovered by chance, and few are known to interact directly with DNA. It is therefore unclear how the histone genes are specifically targeted for unique and coordinated regulation. To expand the list of known HLB factors, we performed a candidate-based screen by mapping 30 publicly available ChIP datasets and 27 factors to the Drosophila histone gene array. We identified novel transcription factor candidates, including the Drosophila Hox proteins Ultrabithorax, Abdominal-A and Abdominal-B, suggesting a new pathway for these factors in influencing body plan morphogenesis. Additionally, we identified six other transcription factors that target the histone gene array: JIL-1, Hr78, the long isoform of fs(1)h as well as the generalized transcription factors TAF-1, TFIIB, and TFIIF. Our foundational screen provides several candidates for future studies into factors that may influence histone biogenesis. Further, our study emphasizes the powerful reservoir of publicly available datasets, which can be mined as a primary screening technique.

molecular biology↗

bioRxiv2022

O-GlcNAc glycosylation orchestrates fate decision and niche function of bone marrow stromal progenitors

In mammals, interactions between the bone marrow (BM) stroma and hematopoietic progenitors contribute to bone-BM homeostasis. Perinatal bone growth and ossification provide a microenvironment for the transition to definitive hematopoiesis; however, mechanisms and interactions orchestrating the development of skeletal and hematopoietic systems remain largely unknown. Here, we establish intracellular O-linked {beta}-N-acetylglucosamine (O-GlcNAc) modification as a posttranslational switch that dictates the differentiation fate and niche function of early BM stromal cells (BMSCs). By modifying and activating RUNX2, O-GlcNAcylation promotes osteogenic differentiation of BMSCs and stromal IL-7 expression to support lymphopoiesis. In contrast, C/EBP{beta}-dependent marrow adipogenesis and expression of myelopoietic stem cell factor (SCF) is inhibited by O-GlcNAcylation. Ablating O-GlcNAc transferase (OGT) in BMSCs leads to impaired bone formation, increased marrow adiposity, as well as defective B-cell lymphopoiesis and myeloid overproduction in mice. Thus, the balance of osteogenic and adipogenic differentiation of BMSCs is determined by reciprocal O-GlcNAc regulation of transcription factors, which simultaneously shapes the hematopoietic niche.

physiology↗

bioRxiv2022

Resilient CD8+ T cells maintain a high cytotoxic capacity by balancing ROS via ME1 upregulation

Cytotoxic T lymphocytes (CTL) are indispensable in anti-tumor immunity. Although CTLs are prone to exhaustion in patients with advanced cancer, T cell resiliency explains the presence of tumor-reactive CTLs that are less exhausted, capable of cytolytic function, expansion, and rebound in response to immunotherapy to reject metastatic malignances. However, the features of resilient T cells have not been clearly defined. In this report, we demonstrate that peripheral CX3CR1+ CD8+ T cells with low mitochondrial membrane potential rebounded CTL function quickly after radiation therapy in patients with large tumor burden portraying their functional resiliency. Furthermore, CX3CR1+ CD8+ T cell with low, but not high, mitochondrial membrane potential are highly cytotoxic, accumulate less reactive oxygen species (ROS), and express more Malic enzyme 1 (ME1). ME1 overexpression increases ATP production in a glycolysisindependent manner while concurrently curtailing excessive ROS in activated CD8+ T cells; and expands CX3CR1+NKG7+ effector CD8+ T cells with enhanced cytotoxicity. Importantly, transfection of ME1 mRNA promotes tumoricidal activity in CD8+ T cells from patients with advanced cancers. Our study reveals a mechanism used by CTLs to balance excessive ROS via ME1 to maintain a metabolic and functional resiliency. Modification of ME1 expression in CTLs may be a novel method to improve the efficacy of cancer immunotherapy by preventing T cell exhaustion. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/517988v3_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@12e0f36org.highwire.dtl.DTLVardef@e8d412org.highwire.dtl.DTLVardef@1baf57eorg.highwire.dtl.DTLVardef@14b2b43_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICX3CR1+ and low Dy m identify functional resilient CD8+ T cells. C_LIO_LIResilient CD8+ T cells are highly cytotoxic and have less ROS. C_LIO_LIResilient CD8+ T cells express more ME1 that can balance extra ROS. C_LIO_LIME1 overexpression can promote CTL function of CD8+ T cells. C_LI

immunology↗

bioRxiv2022

A compendium of genetic regulatory effects across pig tissues

The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.

genomics↗

bioRxiv2022

Schwann cells promote sensory neuron excitability during development

Excitability--the ability to fire action potentials--is a signature feature of neurons. How neurons become excitable during development, and whether excitability is an intrinsic property of neurons or requires signaling from glial cells, remain unclear. Here we demonstrate that Schwann cells, the most abundant glia in the peripheral nervous system, promote somatosensory neuron excitability during development. We find that Schwann cells secrete prostaglandin E2, which is necessary and sufficient to induce developing somatosensory neurons to express normal levels of voltage-gated sodium channels and fire action potential trains. Inactivating this signaling pathway specifically in Schwann cells selectively impairs the maturation of nociceptor and proprioceptor somatosensory neuron subtypes, leading to corresponding sensory defects in thermoception, inflammatory pain, and proprioception. Our studies thus reveal a cell non-autonomous mechanism by which glia regulate neuronal excitability to enable the development of normal sensory functions.

neuroscience↗

bioRxiv2022

Spatially Resolved Single-cell Translatomics at Molecular Resolution

The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolutions. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation for thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in the adult mouse brain tissue with a spatial cell atlas of 62,753 cells. This spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.

genomics↗

bioRxiv2022

Cellular proteomic profiling using proximity labelling by TurboID-NES in microglial and neuronal cell lines.

1.0Different brain cell types play distinct roles in brain development and disease. Molecular characterization of cell-specific mechanisms using cell type-specific approaches at the protein (proteomic) level, can provide biological and therapeutic insights. To overcome the barriers of conventional isolation-based methods for cell type-specific proteomics, in vivo proteomic labeling with proximity dependent biotinylation of cytosolic proteins using biotin ligase TurboID, coupled with mass spectrometry (MS) of labeled proteins, has emerged as a powerful strategy for cell type-specific proteomics in the native state of cells without need for cellular isolation. To complement in vivo proximity labeling approaches, in vitro studies are needed to ensure that cellular proteomes using the TurboID approach are representative of the whole cell proteome, and capture cellular responses to stimuli without disruption of cellular processes. To address this, we generated murine neuroblastoma (N2A) and microglial (BV2) lines stably expressing cytosolic TurboID to biotinylate the cellular proteome for downstream purification and analysis using MS. TurboID-mediated biotinylation captured 59% of BV2 and 65% of N2A proteomes under homeostatic conditions. TurboID expression and biotinylation minimally impacted homeostatic cellular proteomes of BV2 and N2A cells, and did not affect cytokine production or mitochondrial respiration in BV2 cells under resting or lipopolysaccharide (LPS)-stimulated conditions. These included endo-lysosome, translation, vesicle and signaling proteins in BV2 microglia, and synaptic, neuron projection and microtubule proteins in N2A neurons. The effect of LPS treatment on the microglial proteome was captured by MS analysis of biotinylated proteins (>500 differentially-abundant proteins) including increased canonical pro-inflammatory proteins (Il1a, Irg1, Oasl1) and decrease anti-inflammatory proteins (Arg1, Mgl2).

neuroscience↗

bioRxiv2022

Spatiotemporally resolved transcriptomics reveals subcellular RNA kinetic landscape

Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function1. However, the intricate subcellular dynamics of RNA synthesis, decay, export, and translocation remain obscured due to the limitations of existing transcriptomics methods2-8. Here, we report a spatiotemporally resolved RNA mapping method (TEMPOmap) to uncover subcellular RNA profiles across time and space at the single-cell level in heterogeneous cell populations. TEMPOmap integrates pulse-chase metabolic labeling of the transcriptome with highly multiplexed three-dimensional (3D) in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape of 991 genes in human HeLa cells from upstream transcription to downstream subcellular translocation. Clustering analysis of critical RNA kinetic parameters across single cells revealed kinetic gene clusters whose expression patterns were shaped by multi-step kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated to serve molecular and cellular functions in cell-cycle dependent manner. Together, these single-cell spatiotemporally resolved transcriptomics measurements provide us the gateway to uncover new gene regulation principles and understand how kinetic strategies enable precise RNA expression in time and space.

genomics↗

bioRxiv2022

BigNeuron: A resource to benchmark and predict best-performing algorithms for automated reconstruction of neuronal morphology

BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.

bioinformatics↗