Biology subjects

Kuang, X.

Publications and source records attributed to Kuang, X..

18 recordsLinked to original sources

bioRxiv2026

Embeddings from standardized sorghum leaf images capture variation in disease response that human scoring misses

Ordinal scoring of plant disease severity by human raters compresses variation in lesion color, size, and number. Inter-rater variability further complicates comparisons and integrated analyses across environments. We developed a low-cost portable imaging chamber to rapidly image large numbers of leaves under standardized lighting, orientation, and backdrops in the field and employed this system to image more than 11,000 leaves across three states. Embeddings from vision encoders predicted human-assigned disease severity scores. No significant GWAS hits were identified using human-assigned or vegetation-index-based disease severity scores, but GWAS using embeddings identified twelve genomic hotspots controlling leaf appearance. Nine were linked to variation in disease symptom severity. Five hotspots corresponded to previously characterized sorghum genes: all three hotspots not linked to disease and two of the nine that were. Roughly one-third of tested embedding--hotspot associations replicated across at least two states, and twenty replicated across all three. eQTL, PheWAS, and large-effect variant analyses identified single candidate genes with plausible mechanistic links to disease symptom severity for six of the seven hotspots not mapping to characterized genes. These results demonstrate the power of combining scalable, standardized leaf imaging with pretrained image encoders to capture genetically controlled variation in diverse disease symptoms that human ordinal scoring misses.

plant biology↗

bioRxiv2025

Structure-based Predictions of Conformational B Cell Epitopes by Protein Language Model and Deep Learning

Mapping conformational B-cell epitopes remains a central challenge for antibody discovery: experiments are costly and most computational tools trained on generic protein-protein interfaces transfer poorly to antibody-antigen recognition. We introduce a patch-centric framework that predicts epitopes directly on antigen structures. Each surface "patch" is defined as a triad of neighboring residues, capturing the smallest local unit that encodes both shape and chemistry. We evaluate two classifiers: (i) a protein language model (PLM) approach that averages ESM-2 embeddings over each triad and scores them with a small multilayer perceptron [1], and (ii) a convolutional baseline that consumes a hand-crafted 15x20 feature matrix summarizing amino-acid identity, secondary structure, solvent accessibility, and shape index. Trained with five-fold cross-validation on 1,151 AbDb antibody-antigen complexes, the PLM model markedly outperforms the CNN at the patch level (e.g., F1{approx} 0.986, ROC-AUC{approx} 0.998). Aggregating patch scores to residues with an ensemble over all folds yields robust residue-wise performance, surpassing the CNN (ROC-AUC 0.689{+/-}0.072 vs. 0.548{+/-}0.018). Against widely used sequence- and structure-based tools on AbDb, our PLM achieves the best summary metrics (ROC-AUC 0.67, PR- AUC 0.56) with full coverage of all antigens. On five external complexes unseen during development, the model generalizes well (ROC-AUC 0.663) and accurately localizes binding regions qualitatively. The method converts PLM representations into interpretable epitope likelihood maps, offering a practical aid for antigen prioritization, antibody engineering, and vaccine design.

bioinformatics↗

bioRxiv2025

Integrated Classification of Cortical Cells and Quantitative Projectomic Mapping Unveil Organizational Principles of Brain-Wide Connectomes at Single Cell Level

Molecularly defined cortical cell types have recently been linked to whole neuronal morphology (WNM), particularly those characterized by whole-brain-wide projections, such as intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) neurons. In contrast, classical morphological classifications (e.g., tufted TPC, small tufted SPC, and stellate SSC) are based primarily on local dendrosomatic and axonal structures, especially apical dendrites. This study bridges these perspectives by establishing a new neuronal taxonomy, analyzing the connectomes of defined cortical cell types, and comparing them with those obtained from bulk anterograde injections. Neurons were sparsely labeled via tamoxifen-inducible Cre lines with GFP reporters, and 1,419 WNM cells were comprehensively reconstructed with Vaa3D-TeraVR from [~]15 areas across six functional regions of molecularly labeled brains imaged with 2p-fMOST. These cells were newly classified by integrating current molecular-WNM and classical morphological perspectives, with sample size augmented by 1,455 publicly available WNM cells reconstructed from the Mouse-Light project and CEBSIT. This effort defined ten combined molecular-WNM-classical morphological cell types: L5ET_TPC, L6CT_NPC, L6b_HPC, and seven IT types--L2/3IT_TPC, L4IT_SSC, L4IT_UPC, L4IT_TPC, L5IT_SPC, L6IT_IPC, and L6IT_car3PC. Clustering, quantitative analyses and random Forest classifier objectively validated these types and revealed their distinct connectomes, along with convergent, topographic, and hierarchical organizations across their projection brain regions. At the single-cell level, multiple organizational principles governing cortico-cortical (C-C) and cortico-subcortical (C-subC) connectomes emerged with unprecedented detail, offering a precise GPS-like tool for in vivo recordings and robust datasets for neuronal network modeling. Comparisons with bulk anterograde injection data underscored the limitations of traditional methods in identifying projection targets. Overall, our approach provides significant insights into cortical circuitry and elucidates the complex interplay between neuronal molecular identity, whole morphology, and classical morphological classification.

neuroscience↗

bioRxiv2025

SuperMetal: A Generative AI Framework for Rapid and Precise Metal Ion Location Prediction in Proteins

Metal ions, as abundant and vital cofactors in numerous proteins, are crucial for enzymatic activities and protein interactions. Given their pivotal role and catalytic efficiency, accurately and efficiently identifying metal-binding sites is fundamental to elucidating their biological functions and has significant implications for protein engineering and drug discovery. To address this challenge, we present SuperMetal, a generative AI framework that leverages a score-based diffusion model coupled with a confidence model to predict metal-binding sites in proteins with high precision and efficiency. Using zinc ions as an example, SuperMetal outperforms existing state-of-the-art models, achieving a precision of 94 % and coverage of 90 %, with zinc ions localization within 0.52 {+/-} 0.55 [A] of experimentally determined positions, thus marking a substantial advance in metal-binding site prediction. Furthermore, SuperMetal demonstrates rapid prediction capabilities (under 10 seconds for proteins with[~] 2000 residues) and remains minimally affected by increases in protein size. Notably, SuperMetal does not require prior knowledge of the number of metal ions--unlike AlphaFold 3, which depends on this information. Additionally, SuperMetal can be readily adapted to other metal ions or repurposed as a probe framework to identify other types of binding sites, such as protein-binding pockets.

bioinformatics↗

bioRxiv2025

Discovering new amyloid-like peptides using all-atom simulations and artificial intelligence.

Establishing the fundamental relationships between peptide sequences and fibril formation is critical both for understanding protein misfolding processes and for guiding biomaterial design. Here, we combine all-atom molecular dynamics (MD) simulations with artificial intelligence (AI) to investigate how subtle variations in the arrangement of a short peptide sequence affect its propensity to form fibrils. Our results show that small shifts in the distribution of hydrophobic residues and charge clusters can significantly influence both the nucleation rate and the stability of cross-{beta} structures. To rapidly extend this analysis over a wide sequence space, we developed an active learning-enhanced framework--Machine Learning for Molecular Dynamics (ML4MD)--that iteratively refines its predictions based on MD-derived aggregation data. ML4MD efficiently screens numerous peptide permutations and guides the discovery of previously unrecognized fibril-prone sequences, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.939. Overall, ML4MD streamlines the rational design of amyloid-like peptides by integrating detailed atomistic simulations with rapid and high-accuracy ML predictions.

biophysics↗

bioRxiv2024

Genes and pathways determining flowering time variation in temperate adapted sorghum

The timing of the transition from vegetative to reproductive growth is determined by a complex genetic architecture integrating signals from a diverse set of external and internal stimuli and plays a key role in determining plant fitness and adaptation. However, significant divergence in the identities and functions of many flowering time pathway components has been reported among plant species. Here we employ a combination of genome and transcriptome wide association studies to identify genetic determinants of variation in flowering time across multiple environments in a large panel of primarily photoperiod-insensitive sorghum (Sorghum bicolor), a major crop that has, to date, been the subject of substantially less genetic investigation than its relatives. Gene families that form core components of the flowering time pathway in other species, FT-like and SOC1-like genes, appear to play similar roles in sorghum, but the genes identified are not orthologous to the primary FT-like or SOC1-like genes which play similar roles in related species. The aging pathway appears to play a significant role in determining non-photoperiod determined variation in flowering time in sorghum. Two components of this pathway were identified in a transcriptome wide association study, while a third was identified via genome wide association. Our results demonstrate that, while the functions of larger gene families are conserved, functional data from even closely related species is not a reliable guide to which gene copies will play roles in determining natural variation in flowering time.

plant biology↗

bioRxiv2024

SuperWater: Predicting Water Molecule Positions on Protein Structures by Generative AI

Water molecules play a significant role in maintaining protein structural stability and facilitating molecular interactions. Accurate prediction of water molecule positions around protein structures is essential for understanding their biological roles and has significant implications for protein engineering and drug discovery. Here, we introduce SuperWater, a novel generative AI framework that integrates a score-based diffusion model with equivariant graph neural networks to predict water molecule placements around proteins with high accuracy. SuperWater surpasses existing methods, delivering state-of-the-art performance in both crystal water coverage and prediction precision, achieving water localization within 0.3 {+/-} 0.06 {degrees}A of experimentally validated positions. We demonstrate the capabilities of SuperWater through case studies involving protein hydration, protein-ligand binding, and protein-protein binding sites. This framework can be adapted for various applications, including structural biology, binding site prediction, multi-body docking, and water-mediated drug design.

bioinformatics↗

bioRxiv2024

Macrophages facilitate interclonal cooperation-induced tumor heterogeneity and malignancy by activating the innate immune signaling

Tumor heterogeneity is a common hallmark of cancer and is considered a major cause of treatment failure and relapse, yet it remains poorly understood how various types of cells communicate within the tumor microenvironment (TME) to regulate tumor progression in vivo. Here we establish a tumor heterogeneity model in Drosophila eye epithelium by mutating the tricellular junction protein M6 in cells surrounding RasV12benign tumors and dissect the in vivo mechanisms underlying interclonal cooperation-induced malignancy by utilizing sophisticated genetic techniques in conjunction with single-cell RNA sequencing (scRNA-seq). Our findings reveal that loss of M6 facilitates the malignant transformation of neighboring RasV12 tumors by activating the Toll signaling, the innate immune response pathway. Notably, inhibiting Toll signaling impedes tumor progression, whereas its activation synergistically promotes RasV12 tumor malignancy by inactivating the Hippo pathway. Mechanistically, RasV12 tumors surrounded by M6 mutant clones lead to increased recruitment of hemocytes, which are the equivalent of macrophages in Drosophila, in a JNK pathway-dependent manner. Consequently, these tumor-associated macrophages secrete the Spatzle (Spz) ligand, which subsequently activates the Toll receptor within the RasV12 tumors, thereby triggering tumorigenesis. In summary, our study elucidates the complex in vivo interactions between genetically distinct oncogenic cells and between tumors and macrophages, shedding light on how macrophages exploit the innate immune signaling within tumors to regulate tumor heterogeneity and promote tumor progression. Significance statementIntratumoral heterogeneity profoundly affects cancer development and treatment in human tumors. The intricate nature of tumor cells and the presence of diverse cell types pose challenges to uncovering in vivo mechanisms responsible for heterogeneity. Our Drosophila tumor heterogeneity model reveals that fruit fly macrophages promotes both tumor heterogeneity and malignancy. Following recruitment by tumor cells, these macrophages secrete the ligand Spz to activate the Toll signaling pathway within tumor cells, which subsequently inactivates the Hippo pathway to drive tumorigenesis. Our study highlights the crucial role of hemocytes as intermediaries in coordinating tumor heterogeneity and facilitating intercellular communication between different cells within the TME.

cancer biology↗

bioRxiv2023

Connecting single neuron transcriptomes to the projectome in mouse visual cortex

The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neurons role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morphoelectric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.

neuroscience↗

bioRxiv2022

USP22 overexpression fails to augment tumor formation in MMTV-ERBB2 mice but loss of function impacts MMTV promoter activity

The Ubiquitin Specific Peptidase 22 (USP22), a component of the Spt-Ada-Gcn5 Acetyltransferase (SAGA) histone modifying complex, is overexpressed in multiple human cancers, but how USP22 impacts tumorigenesis is not clear. We reported previously that Usp22 loss in mice impacts execution of several signaling pathways driven by growth factor receptors such as erythroblastic oncogene B b2 (ERBB2). To determine whether changes in USP22 expression affects ERBB2-driven tumorigenesis, we introduced conditional overexpression or deletion alleles of Usp22 into mice bearing the Mouse mammary tumor virus-Neu-Ires-Cre (MMTV-NIC) transgene, which drives both rat ERBB2/NEU expression and Cre recombinase activity from the MMTV promoter resulting in mammary tumor formation. We found that USP22 overexpression in mammary glands did not further enhance primary tumorigenesis in MMTV-NIC female mice, but increased lung metastases were observed. However, deletion of Usp22 significantly decreased tumor burden and increased survival of MMTV-NIC mice. These effects were associated with markedly decreased levels of both Erbb2 mRNA and protein, indicating Usp22 loss impacts MMTV promoter activity. Usp22 loss had no impact on ERBB2 expression in other settings, including MCF10A cells bearing a Cytomegalovirus (CMV) - driven ERBB2 transgene or in human epidermal growth factor receptor 2 (HER2)+ human SKBR3 and HCC1953 cells. Decreased activity of the MMTV promoter in MMTV-NIC mice correlated with decreased expression of known regulatory factors, including the glucocorticoid receptor (GR), the progesterone receptor (PR), and the chromatin remodeling factor Brahma-related gene-1 (BRG1). Together our findings indicate that increased expression of USP22 does not augment the activity of an activated ERBB2/NEU transgene but impacts of Usp22 loss on tumorigenesis cannot be assessed in this model due to unexpected effects on MMTV-driven Erbb2/Neu expression.

cancer biology↗

bioRxiv2022

Regional and cell type-specific afferent and efferent projections of the mouse claustrum

The claustrum (CLA) is a conspicuous subcortical structure interconnected with cortical and subcortical regions. However, its regional anatomy and cell-type-specific connections in the mouse remain largely undetermined. Here, we accurately delineated the boundary of the mouse CLA and quantitatively investigated its inputs and outputs brain-wide using anterograde and retrograde viral tracing and fully reconstructed single claustral principal neurons. At a population level, the CLA reciprocally connects with all isocortical modules. It also receives inputs from at least 35 subcortical structures but sends projections back to only a few of them. We found that cell types projecting to the CLA are differentiated by cortical areas and layers. We classified single CLA principal neurons into at least 9 cell types that innervate the diverse sets of functionally linked cortical targets. Axons of interneurons within the CLA arborize along almost its entire anteroposterior extent. Together, this detailed wiring diagram of the cell-type-specific connections of the mouse CLA lays a foundation for studying its functions.

neuroscience↗

bioRxiv2020

Computable Early C. elegans Embryo with a Data-driven Phase Field Model

Morphogenesis is a precise and robust dynamic process during metazoan embryogenesis, consisting of both cell proliferation and cell migration. Despite the fact that much is known about specific regulations at the molecular level, how cell proliferation and migration together drive the morphogenesis at the cellular and organismic levels is not well understood. Here, using Caenorhabditis elegans as the model animal, we present a data-driven phase field model to compute the early embryonic morphogenesis within a confined eggshell. By using three-dimensional time-lapse cellular morphological information generated by imaging experiments to set the model parameters, we can not only reproduce the precise evolution of cell location, cell shape and cell-cell contact relationship in vivo, but also reveal the critical roles of cell division and cell-cell attraction in governing the early development of C. elegans embryo. In brief, we provide a generic approach to compute the embryonic morphogenesis and decipher the underlying mechanisms.

systems biology↗

bioRxiv2020

Cellular Anatomy of the Mouse Primary Motor Cortex

An essential step toward understanding brain function is to establish a cellular-resolution structural framework upon which multi-scale and multi-modal information spanning molecules, cells, circuits and systems can be integrated and interpreted. Here, through a collaborative effort from the Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based description of one brain structure - the primary motor cortex upper limb area (MOp-ul) of the mouse. Applying state-of-the-art labeling, imaging, computational, and neuroinformatics tools, we delineated the MOp-ul within the Mouse Brain 3D Common Coordinate Framework (CCF). We defined over two dozen MOp-ul projection neuron (PN) types by their anterograde targets; the spatial distribution of their somata defines 11 cortical sublayers, a significant refinement of the classic notion of cortical laminar organization. We further combine multiple complementary tracing methods (classic tract tracing, cell type-based anterograde, retrograde, and transsynaptic viral tracing, high-throughput BARseq, and complete single cell reconstruction) to systematically chart cell type-based MOp input-output streams. As PNs link distant brain regions at synapses as well as host cellular gene expression, our construction of a PN type resolution MOp-ul wiring diagram will facilitate an integrated analysis of motor control circuitry across the molecular, cellular, and systems levels. This work further provides a roadmap towards a cellular resolution description of mammalian brain architecture.

neuroscience↗

bioRxiv2020

Negative effects characterization and comparative transcriptomics elucidation on the lag phase of an industrial S. cerevisiae under the corn stover hydrolysate stress

During biofuels fermentation from pretreated lignocellulosic biomass, the strong toxicity of the lignocellulose hydrolysate is resulted from the synergistic effect of multiple lignocellulosic inhibitors, which far exceeds the sum of effects caused by every single inhibitor. Meanwhile, the synergistic effect is unclear and the underlying response mechanism of the industrial yeast towards the actual pretreated lignocellulose hydrolysate is still under exploration. Here, we employed an industrial S. cerevisiae for the transcriptomic analysis in two time points (early and late) of the lag phase under the corn stover hydrolysate stress. As investigation, the corn stover hydrolysate caused the accumulation of reactive oxygen species (ROS), damages of mitochondrial membrane and endoplasmic reticulum (ER) membrane in the industrial S. cerevisiae YBA_08 during the lag phase, especially these negative effects were more significant at the early lag phase. Based on the transcriptome profile, the industrial S. cerevisiae YBA_08 might recruit stress-related transcription factors (MSN4, STE12, SFL1, CIN5, COM2, MIG3, etc.) through the mitogen-activated protein kinase (MAPK)-signaling pathway to induce a transient G1/G2 arrest, and to activate defense bioprocesses like protectants metabolism, sulfur metabolism, glutaredoxin system, thioredoxin system, heat shock proteins chaperone and oxidoreductase detoxification, resisting those compounded stresses including oxidative stress, osmotic stress and structural stress. Surprisingly, this defense system might be accompanied with the transient repression of several bioprocesses like fatty acid metabolism, purine de novo biosynthesis and ergosterol biosynthesis. ImportanceThis research systematically demonstrated the lag phase response of an industrial yeast to the lignocellulosic hydrolysate in transcriptional level, providing a molecular fundament for understanding the synergistic effect of various lignocellulosic inhibitors and the regulatory mechanism of tolerance for industrial yeasts under this stress.

microbiology↗

bioRxiv2019

Tuning cell motility via cell tension with a mechanochemical cell migration model

Cell migration is orchestrated by a complicated mechanochemical system. However, few cell migration models take account of the coupling between a biochemical network and mechanical factors. Here, we construct a mechanochemical cell migration model to study the cell tension effect on cell migration. Our model incorporates the interactions between Rac-GTP, Rac-GDP, F-actin, myosin, and cell tension, and it is based on phase field approach hence very convenient in describing the cell shape change. This model captures common features of cell polarization, cell shape change, and cell migration modes. It shows cell tension inhibits migration ability monotonically when cells are applied with persistent external stimuli. On the other hand, if random internal noise is significant, the regulation of cell tension exerts a non-monotonic effect on cell migration. As the elevation of cell tension impedes the formation of multiple protrusions hence enhances the streamline position of the cell body. Therefore the migration ability could be maximized at intermediate cell tension under random internal noise. These model predictions are consistent with our singlecell experiments and other experimental results. Statement of significanceCell migration plays a vital role in many biological processes such as tumor metastasis. It is a complicated process regulated by dynamic coupling between the biochemical network and mechanical forces. However, few cell migration models take account of both factors. Here, we construct a mechanochemical cell migration model to study how cell migration is regulated by cell tension. Our model predicts that cell tension not only inhibits cell movement under persistent external stimuli but also prompts cell migration under random internal noise when cell tension is low. Therefore an optimized cell tension could maximize the migration ability under random internal noise. We further confirmed these model predictions are consistent with our single-cell experiments and other published experimental results.

biophysics↗

bioRxiv2019

In utero estrogenic endocrine disruption alters the stroma to increase extracellular matrix density and mammary gland stiffness.

In utero endocrine disruption is linked to increased risk of breast cancer later in life. Despite numerous studies establishing this linkage, the long-term molecular changes that predispose mammary cells to carcinogenic transformation are unknown. Several lines of evidence indicate the stroma mediates endocrine disruption following early-life (or in utero) exposure. Herein, we utilized BPA as a model of estrogenic endocrine disruption to analyze the long-term consequences in the stroma. Using RNA-seq transcriptional profiling of adult primary fibroblasts isolated from female mice exposed to BPA in utero, we identified deregulated genes associated with the extracellular matrix. Specifically, multiple collagen genes had increased expression in exposed mice. In line with the transcriptional data, collagen deposition is increased in adult BPA-exposed mice. We further demonstrate in vitro that fibroblasts exposed to BPA in utero remodel a collagen matrix, thereby decreasing permeability of the collagen matrix. These alterations to the mammary gland resulted in increased gland stiffness in the adult mice. Our data connects early life endocrine disruption to breast density. Interestingly, increased collagen deposition and gland stiffness were not observed in the developing glands of younger mice, suggesting risk factors for breast cancer continue to develop throughout life following these exposures. Finally, we assessed whether in utero exposure to two other endocrine disruptors, BPS and DES, also increase breast stiffness in adult mice. While DES increased breast stiffness, BPS did not, suggesting this BPA alternative may in fact pose less breast cancer risk than its predecessor. As breast stiffness, extracellular matrix density, and collagen deposition have been directly linked to breast cancer risk, these data mechanistically link endocrine disruptor exposures and molecular alterations to increased disease susceptibility in the gland.

cancer biology↗

bioRxiv2019

Complete single neuron reconstruction reveals morphological diversity in molecularly defined claustral and cortical neuron types

Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits.

neuroscience↗

bioRxiv2019

RecV recombinase system for spatiotemporally controlled light-inducible genomic modifications.

Brain circuits are composed of vast numbers of intricately interconnected neurons with diverse molecular, anatomical and physiological properties. To allow highly specific targeting of individual neurons for structural and functional studies, we modified three site-specific DNA recombinases, Cre, Dre and Flp, by combining them with a fungal light-inducible protein, Vivid, so that their recombinase activities can be driven by blue light. We generated viral vectors to express these light-inducible recombinases and demonstrated that they can induce genomic modifications in dense or sparse populations of neurons in live mouse brains controlled by one-photon or two-photon light induction. As an important application, we showed that light-inducible recombinases can produce highly targeted, sparse and strong labeling of individual neurons thereby enabling whole-brain morphological reconstruction to identify their axonal projection specificity. In addition to targeting cortical brain areas, we applied the method in deep targets, with a demonstration of functional calcium imaging. These molecular tools enable spatiotemporally-precise, targeted genomic modifications that will greatly facilitate detailed analysis of neural circuits and linking genetic identity, morphology, connectivity and function.

neuroscience↗