Biology subjects

Chen, A.

Publications and source records attributed to Chen, A..

At least 181 records · Page 10Linked to original sources

bioRxiv2023

Artificial nanovesicles for dsRNA delivery in spray induced gene silencing for crop protection

Spray-Induced Gene Silencing (SIGS) is an innovative and eco-friendly technology where topical application of pathogen gene-targeting RNAs to plant material can enable disease control. SIGS applications remain limited because of the instability of dsRNA, which can be rapidly degraded when exposed to various environmental conditions. Inspired by the natural mechanism of crosskingdom RNAi through extracellular vesicle trafficking, we describe herein the use of artificial nanovesicles (AVs) for dsRNA encapsulation and control against the fungal pathogen, Botrytis cinerea. AVs were synthesized using three different cationic lipid formulations, DOTAP + PEG, DOTAP, and DODMA, and examined for their ability to protect and deliver dsRNA. All three formulations enabled dsRNA delivery and uptake by B. cinerea. Further, encapsulating dsRNA in AVs provided strong protection from nuclease degradation and from removal by leaf washing. This improved stability led to prolonged RNAi-mediated protection against B. cinerea both on pre- and post-harvest plant material using AVs. Specifically, the AVs extended the protection duration conferred by dsRNA to 10 days on tomato and grape fruits and to 21 days on grape leaves. The results of this work demonstrate how AVs can be used as a new nanocarrier to overcome dsRNA instability in SIGS for crop protection.

plant biology↗

bioRxiv2023

A simple and efficient in planta transformation method based on the active regeneration capacity of plants

Plant genetic transformation strategies serve as essential tools for the genetic engineering and advanced molecular breeding of plants. However, the complicated operational protocol and low efficiency of the current transformation strategies restrict the genetic modification of most plant species. This paper describes the development of a Regenerative Activity-dependent in Planta Injection Delivery (RAPID) method based on the active regeneration capacity of plants. In this method, Agrobacterium tumefaciens was delivered to plant meristems via injection for inducing transfected renascent tissues. Stable transgenic plants were obtained by subsequent vegetative propagation of the positive renascent tissues. The method was successfully applied for the transformation of plants with strong regeneration capacity, including different genotypes of sweet potato (Ipomoea batatas), potato (Solanum tuberosum), and bayhops (I. pes-caprae). Compared to the traditional transformation methods, RAPID has a markedly high transformation efficiency (up to ~ 40%), shorter duration (less than 4 weeks), and does not require tissue culture procedures. The RAPID method therefore overcomes the limitations of traditional methods for achieving rapid in planta transformation, and can be potentially applied to a wide range of plant species that are capable of active regeneration.

plant biology↗

bioRxiv2022

StereoSiTE: A framework to spatially and quantitatively profile the cellular neighborhood organized iTME

With emerging of Spatial Transcriptomics (ST) technology, a powerful algorithmic framework to quantitatively evaluate the active cell-cell interactions in the bio-function associated iTME unit will pave the ways to understand the mechanism underlying tumor biology. This study provides the StereoSiTE incorporating open source bioinformatics tools with the self-developed algorithm, SCII, to dissect a cellular neighborhood (CN) organized iTME based on cellular compositions, and to accurately infer the functional cell-cell communications with quantitatively defined interaction intensity in ST data. We applied StereoSiTE to deeply decode ST data of the xenograft models receiving immunoagonist. Results demonstrated that the neutrophils dominated CN5 might attribute to iTME remodeling after treatment. To be noted, SCII analyzed the spatially resolved interaction intensity inferring a neutrophil leading communication network which was proved to actively function by analysis of Transcriptional Factor Regulon and Protein-Protein Interaction. Altogether, StereoSiTE is a promising framework for ST data to spatially reveal tumoribiology mechanisms.

cancer biology↗

bioRxiv2022

Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics

Cells do not live in a vacuum, but in a milieu defined by cell-cell communication that can be measured via emerging high-resolution spatial transcriptomics approaches. However, analytical tools that fully leverage such data for kinetic modeling remain lacking. Here we present Spateo (aristoteleo/spateo-release), a general framework for quantitative spatiotemporal modeling of single-cell resolution spatial transcriptomics. Spateo delivers novel methods for digitizing spatial layers/columns to identify spatially-polar genes, and develops a comprehensive framework of cell-cell interaction to reveal spatial effects of niche factors and cell type-specific ligand-receptor interactions. Furthermore, Spateo reconstructs 3D models of whole embryos, and performs 3D morphometric analyses. Lastly, Spateo introduces the concept of "morphometric vector field" of cell migrations, and integrates spatial differential geometry to unveil regulatory programs underlying various organogenesis patterns of Drosophila. Thus, Spateo enables the study of the ecology of organs at a molecular level in 3D space, beyond isolated single cells.

systems biology↗

bioRxiv2022

Analysis of spatial transcriptomics at varying resolution levels using the unified framework of SpaSEG

Spatially resolved transcriptomics (SRT) for characterizing cellular heterogeneities and activities requires systematic analysis approaches to decipher gene expression variations in physiological contexts. Here we develop SpaSEG, an unsupervised convolutional neural network-based model for multiple SRT analysis tasks by jointly learning the transcriptional similarity of spots and their spatial dependence. SpaSEG adopts an edge strength constraint to encourage spatial domain coherence and allows integrative analysis by automatically aligning the spatial domains across multiple adjacent sections. It also enables the detection of domain-specific gene expression patterns and the inference of intercellular interactions and colocalizations within a tissue. In an invasive ductal carcinoma sample analysis, SpaSEG facilitates the unraveling of intratumor heterogeneity and the understanding of immunoregulatory mechanisms. Through comprehensive evaluation over a collection of SRT datasets generated by different platforms at various resolutions, SpaSEG shows superior reliability and computational efficiency over existing methods, endowing it with a great potential for the exploration of tissue architectures and pathological biology.

bioinformatics↗

bioRxiv2022

Integrated transcriptome and lineage analyses reveal novel catecholaminergic cardiomyocytes contributing to the cardiac conduction system in murine heart

Cardiac conduction system (CCS) morphogenesis is essential for correct heart function yet is incompletely understood. Here we established the transcriptional landscape of cell types populating the developing heart by integrating single-cell RNA sequencing and spatial enhanced resolution omics-sequencing (Stereo-seq). Stereo-seq provided a spatiotemporal transcriptomic cell fate map of the murine heart with a panoramic field of view and in situ cellular resolution of the CCS. This led to the identification of a previously unrecognized cardiomyocyte population expressing dopamine beta-hydroxylase (Dbh+-CMs), which is closely associated with the CCS in transcriptomic analyses. To confirm this finding, genetic fate mapping by using DbhCre/Rosa26-tdTomato reporter mouse line was performed with Stereo-seq, RNAscope, and immunohistology. We revealed that Dbh+-derived CMs first emerged in the sinus venosus at E12.5, then populated the atrial and ventricular CCS components at E14.5, with increasing abundance towards perinatal stages. Further tracing by using DbhCFP reporter and DbhCreERT/Rosa26-tdTomato inducible reporter, we confirmed that Dbh+-CMs are mostly abundant in the AVN and ventricular CCS and this persists in the adult heart. By using DbhCre/Rosa26-tdTomato/Cx40-eGFP compound reporter line, we validated a clear co-localization of tdTomato and eGFP signals in both left and right ventricular Purkinje fibre networks. Finally, electrophysiological optogenetic study using cell-type specific Channelrhodopsin2 (ChR2) expression further elucidated that Dbh+-derived CMs form a functional part of the ventricular CCS and display similar photostimulation-induced electrophysiological characteristics to Cx40CreERT/ChR2-tdTomato CCS components. Thus, by utilizing advanced transcriptomic, mouse genetic, and optogenetic functional analyses, our study provides new insights into mammalian CCS development and heterogeneity by revealing novel Dbh+-CMs. HighlightsO_LIStereo-seq provided a spatiotemporal transcriptomic cell fate map of the murine heart with a panoramic field of view and in situ cellular resolution of the CCS. C_LIO_LIEstablished the transcriptional landscape of cell types populating the developing murine heart. C_LIO_LIRevealed previously unreported catecholaminergic cardiomyocyte populations contributing to the developing and mature murine cardiac conduction system. C_LI

cell biology↗

bioRxiv2022

Development of an affinity-enhanced clinical candidate TCR targeting NY-ESO-1 with optimal potency and high specificity

The clinical success of T-cell receptor (TCR)-based immunotherapy depends on the efficacy and specificity of TCRs. Naturally occurring TCRs have limited anti-tumor potency due to their low affinity for tumor antigens. Affinity enhancement is a promising strategy to generate highly potent TCRs. However, it is concerned that affinity-enhanced TCRs are prone to lose specificity. We isolated low affinity TCRs specific for NY-ESO-1157-165/HLA-A*02:01 from peripheral blood mononuclear cells of healthy donors. An affinity-enhanced TCR candidate with optimal affinity and specificity was generated using phage display and an extensive set of in vitro and in vivo assays. Alanine scanning mutagenesis showed that the TCR candidate retained specificity by making extensive contacts to the side chains of NY-ESO-1157-165 peptide. Adoptive transfer of T cells engineered with this candidate (termed TAEST16001) significantly inhibited tumor growth in subcutaneous, metastatic, and patient-derived xenograft (PDX) mouse tumor models. This study demonstrates that sophisticated engineering and screening techniques can be utilized to generate a clinical candidate TCR with potent anti-tumor activity without losing specificity. TAEST16001 was approved by the Center for Drug Evaluation (CDE) as the first TCR-based immunotherapy clinical trial in China (ClinicalTrials.gov Identifier: NCT03159585).

immunology↗

bioRxiv2022

HDAC8-mediated inhibition of EP300 drives a neural crest-like transcriptional state that increases melanoma brain metastasis

Melanomas are heterogeneous and adopt multiple transcriptional states that can confer an invasive phenotype and resistance to therapy. Little is known about the epigenetic drivers of these cell states, limiting our ability to regulate melanoma heterogeneity and tumor progression. Here we identify stress-induced HDAC8 activity as the driver of a neural crest stem cell (NCSC)-like transcriptional state that increased the formation of melanoma brain metastases (MBM). Exposure of melanocytes and melanoma cells to multiple different stresses led to HDAC8 activation, a switch to a NCSC gene expression signature and the adoption of an amoeboid, invasive phenotype. This cell state enhanced the survival of melanoma cells under shear stress conditions and increased the formation of metastases in the brain. scRNA-seq analyses showed that HDAC8 expression was correlated with the NCSC cell state in clinical MBM specimens. ATAC-Seq and ChIP-Seq analysis showed HDAC8 to alter chromatin structure by increasing H3K27ac and accessibility at c-Jun binding sites without changing global histone acetylation. The increased accessibility of Jun binding sites was paralleled by decreased H3K27ac and accessibility at MITF binding sites and loss of melanoma-lineage gene expression. Mass spectrometry-based acetylomics demonstrated that HDAC8 deacetylated the histone acetyltransferase (HAT) EP300 leading to its enzymatic inactivation. This, in turn, led to an increased binding of EP300 to Jun-transcriptional sites and decreased binding to MITF-transcriptional sites. Increased expression of EP300 decreased invasion and increased the sensitivity of melanoma cells to multiple stresses while inhibition of EP300 function increased invasion and resistance to stress. We identified HDAC8 as a novel mediator of transcriptional co-factor inactivation and chromatin accessibility that increases MBM development.

cancer biology↗

bioRxiv2022

Contrary neuronal recalibration in different multisensory cortical areas

The adult brain demonstrates remarkable multisensory plasticity by dynamically recalibrating information from multiple sensory sources. When a systematic visual-vestibular heading offset is experienced, the unisensory perceptual estimates recalibrate toward each other (in opposite directions) to reduce the conflict. The neural substrate of this recalibration is unknown. Here, we recorded single-neuron activity from the dorsal medial superior temporal (MSTd), parieto-insular vestibular cortex (PIVC), and ventral intraparietal (VIP) areas in three male rhesus macaques during visual-vestibular recalibration. Both visual and vestibular tuning in MSTd recalibrated-each according to their respective cues perceptual shifts. Vestibular tuning in PIVC also recalibrated together with corresponding perceptual shifts (cells were not visually tuned). By contrast, VIP neurons demonstrated a unique phenomenon: both vestibular and visual tuning recalibrated according to vestibular perceptual shifts. Such that, visual tuning shifted, surprisingly, contrary to visual perceptual shifts. Therefore, while unsupervised recalibration (to reduce cue conflict) occurs in early multisensory cortices, higher-level VIP reflects only a global shift, in vestibular space. In BriefThe neural bases of multisensory plasticity are currently unknown. Here, Zeng et al. studied neuronal recalibration to a systematic visual-vestibular cue conflict. In multisensory cortical areas MSTd and PIVC, single-unit responses to visual and vestibular stimuli recalibrated to reduce the cue conflict, along with their respective unisensory perceptual shifts. By contrast, in higher-level VIP, both visual and vestibular neuronal responses recalibrated with vestibular perceptual shifts. This led to a surprising recalibration of visual responses opposite in direction to visual perceptual shifts. This exposes differential aspects of multisensory plasticity across multisensory cortical areas, and reveals a novel hybrid of visual responses within a vestibular reference frame in parietal neurons. HighlightsO_LIIn the presence of a systematic heading conflict, visual and vestibular cues recalibrate towards one another to reduce the conflict. C_LIO_LIIn MSTd, neuronal responses to vestibular and visual cues recalibrated, each according to their respective cues perceptual shifts. C_LIO_LIIn PIVC, vestibular responses recalibrated according to vestibular perceptual shifts (cells were not visually tuned). C_LIO_LIIn VIP, neuronal responses to both vestibular and visual cues recalibrated together with vestibular perceptual shifts (opposite in direction to visual perceptual shifts). C_LIO_LIProfound differences in neuronal recalibration expose different functions across multisensory cortical areas. C_LI

neuroscience↗

bioRxiv2022

3D Ground Truth Annotations of Nuclei in 3D Microscopy Volumes

In this paper we describe a set of 3D microscopy volumes we have partially manually annotated. We describe the volumes annotated and the tools and processes we use to annotate the volumes. In addition, we provide examples of annotated subvolumes. We also provide synthetically generated 3D microscopy volumes that can be used for training segmentation methods. The full set of annotations, synthetically generated volumes, and original volumes can be accessed as described in the paper.

cell biology↗

bioRxiv2022

C4 Grasses Employ Various Strategies to Acclimate Rubisco Activase to Heat Stress

1Rising temperatures due to the current climate crisis will have devastating impacts on crop performance and resilience in the near future. One key step that limits plant photosynthetic performance under higher temperatures is the activity of the thermolabile enzyme rubisco activase (RCA). RCA is highly conserved in photosynthetic organisms, including C4 crops such as Zea mays (maize) and Sorghum bicolor (sorghum) which are crucial components of global food supply and the bioenergy sector. While rubisco is the most abundant protein on earth and responsible for carbon fixation, RCA is an essential chaperone required to remove inhibitory sugar phosphates from the active site of rubisco to allow for continued CO2 fixation. We set out to understand temperature-dependent RCA regulation in four different C4 plants, with a focus on the crop plants maize (two cultivars) and sorghum, as well as the model grass Setaria viridis (setaria). Gas exchange measurements confirm that CO2 assimilation is indeed limited by Ribulose 1,5-bisphosphate (RuBP) carboxylation in these organisms and at high temperatures. All three species express distinct sets of RCA isoforms and each species alters the isoform and proteoform abundances in response to heat; however, the changes are species-specific. In order to understand how even subtle changes in the molecular environment of the chloroplast stroma affect RCA function during heat acclimation, we examined the regulation of RCA activity directly with respect thermostability, the ratio of ADP to ATP and the concentration of Mg2+ ions. As shown previously, the activity of RCA is modulated by a combination of these variables, but surprisingly, how these biochemical environment factors affect RCA function differs vastly between the different C4 species, and differences are even apparent between different cultivars within a single species, both with respect to proteoform abundance and regulation. Our results suggest that each grass evolved different parts of the RCA regulation portfolio and we conclude that a successful engineering approach aimed at improving carbon capture in C4 grasses will need to accommodate these individual regulatory mechanisms.

plant biology↗

bioRxiv2022

Solid/liquid coexistence during aging of FUS condensates

A wide range of macromolecules undergo phase separation, forming biomolecular condensates in living cells. These membraneless organelles are typically highly dynamic, formed in a reversible manner, and carry out important functions in biological systems. Crucially, however, a further liquid-to-solid transition of the condensates can lead to irreversible pathological aggregation and cellular dysfunction associated with the onset and development of neurodegenerative diseases. Despite the importance of this liquid-to-solid transition of proteins, the mechanism by which it is initiated in normally functional condensates is unknown. Here we show, by measuring the changes in structure, dynamics and mechanics in time and space, that FUS condensates do not uniformly convert to a solid gel, but rather that liquid and gel phases co-exist simultaneously within the same condensate, resulting in highly inhomogeneous structures. We introduce two new optical techniques, dynamic spatial mapping and reflective confocal dynamic speckle microscopy, and use these to further show that the liquid-to-solid transition is initiated at the interface between the dense phase within condensates and the dilute phase. These results reveal the importance of the spatiotemporal dimension of the liquid-to-solid transition and highlight the interface of biomolecular condensates as a key element in driving pathological protein aggregation.

biophysics↗

bioRxiv2022

DeepST: A versatile graph contrastive learning framework for spatially informed clustering, integration, and deconvolution of spatial transcriptomics

Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining its spatial context. Effective exploitation of this data combination requires spatially informed analysis tools to perform three key tasks, spatial clustering, multi-sample integration, and cell type deconvolution. Here, we present GraphST, a novel graph self-supervised contrastive learning method that incorporates spatial location information and gene expression profiles to accomplish all three tasks in a streamlined process while outperforming existing methods in each task. GraphST combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. With GraphST, we achieved 10% higher clustering accuracy on multiple datasets than competing methods, and better delineated the fine-grained structures in tissues such as the brain and embryo. Moreover, GraphST is the only method that can jointly analyze multiple tissue slices in both vertical and horizontal integration while correcting for batch effects. Lastly, compared to other methods, GraphSTs cell type deconvolution achieved higher accuracy on simulated data and better captured spatial niches such as the germinal centers of the lymph node in experimentally acquired data. We further showed that GraphST can recover the immune cell distribution in different regions of breast tumor tissue and reveal spatial niches with exhausted tumor infiltrating T cells. Through our examples, we demonstrated that GraphST is widely applicable to a broad range of tissue types and technology platforms. In summary, GraphST is a streamlined, user friendly and computationally efficient tool for characterizing tissue complexity and gaining biological insights into the spatial organization within tissues.

bioinformatics↗

bioRxiv2022

3D CentroidNet: Nuclei Centroid Detection With Vector Flow Voting

Automated microscope systems are increasingly used to collect large-scale 3D image volumes of biological tissues. Since cell boundaries are seldom delineated in these images, detection of nuclei is a critical step for identifying and analyzing individual cells. Due to the large intra-class variability in nuclei morphology and the difficulty of generating ground truth annotations, accurate nuclei detection remains a challenging task. We propose a 3D nuclei centroid detection method by estimating the "vector flow" volume where each voxel represents a 3D vector pointing to its nearest nuclei centroid in the corresponding microscopy volume. We then use a voting mechanism to estimate the 3D nuclei centroids from the "vector flow" volume. Our system is trained on synthetic microscopy volumes and tested on real microscopy volumes. The evaluation results indicate our method outperforms other methods both visually and quantitatively.

cell biology↗

bioRxiv2022

Mixed selectivity coding of content-temporal detail by dorsomedial posterior parietal neurons

The dorsomedial posterior parietal cortex is part of a higher-cognition network implicated in elaborate processes underpinning memory formation, recollection, episodes reconstruction, and temporal information processing. Neural coding for complex episodic processing is however under-documented. Here we revealed a set of neural codes of neuroethogram in the primate parietal cortex. Analyzing neural responses in macaque dmPPC to naturalistic videos, we discovered several groups of neurons that are sensitive to different categories of ethogram-items and to low-level sensory features, and saccadic eye movement. We also discovered that the processing of category and feature information by these neurons is sustained by accumulation of temporal information over a long timescale up to 30 s, corroborating its reported long temporal receptive windows. We performed an additional behavioral experiment and found that saccade-related activities could not account for the mixed neuronal responses elicited by the video stimuli. We further observed monkeys scan-paths and gaze consistency are modulated by video content. Taken altogether, these neural findings explain how dorsomedial PPC weaves fabrics of ongoing experiences together in real-time. The high dimensionality of neural representations should motivate us to shift the focus of attention from pure selectivity neurons to mixed selectivity neurons, especially in increasingly complex naturalistic task designs. HIGHLIGHTSO_LINeural codes for "neuroethogram" in macaque dorsomedial parietal cortex C_LIO_LIParietal neural codes exhibit mixed selectivity of event features C_LIO_LIDorsomedial PPC neurons support a long temporal receptive window for episodes C_LIO_LISaccadic movement could not explain away mixed neuronal responses C_LIO_LIConsistency in scan-path and gaze shown across viewing repetitions C_LI

neuroscience↗

bioRxiv2022

NISNet3D: Three-Dimensional Nuclear Synthesis and Instance Segmentation for Fluorescence Microscopy Images

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, researchers generally segment cells by their nuclei. While effective tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task for which few tools have been developed. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present researchers with the opportunity to characterize entire organs. Methods based upon deep-learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper we describe 3D Nuclei Instance Segmentation Network (NISNet3D), a deep learning-based approach in which training is accomplished using synthetic data, profoundly reducing the effort required for network training. We compare results obtained from NISNet3D with results obtained from eight existing techniques.

cell biology↗

bioRxiv2022

DINAVID: A Distributed and Networked Image Analysis System for Volumetric Image Data

BackgroundThe advancement of high content optical microscopy has enabled the acquisition of very large 3D image datasets. Image analysis tools and three dimensional visualization are critical for analyzing and interpreting 3D image volumes. The analysis of these volumes require more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based/cloud-based 3D image processing system. ResultsThe Distributed and Networked Analysis of Volumetric Image Data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. DINAVID is a server/cloud-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID is designed using open source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis as well as a 3D visualization system. ConclusionsIn this paper, we will present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis.

cell biology↗

bioRxiv2022

An Ensemble Learning and Slice Fusion Strategy for Three-Dimensional Nuclei Instance Segmentation

Automated microscopy image analysis is a fundamental step for digital pathology and computer aided diagnosis. Most existing deep learning methods typically require post-processing to achieve instance segmentation and are computationally expensive when directly used with 3D microscopy volumes. Supervised learning methods generally need large amounts of ground truth annotations for training whereas manually annotating ground truth masks is laborious especially for a 3D volume. To address these issues, we propose an ensemble learning and slice fusion strategy for 3D nuclei instance segmentation that we call Ensemble Mask R-CNN (EMR-CNN) which uses different object detectors to generate nuclei segmentation masks for each 2D slice of a volume and propose a 2D ensemble fusion and a 2D to 3D slice fusion to merge these 2D segmentation masks into a 3D segmentation mask. Our method does not need any ground truth annotations for training and can inference on any large size volumes. Our proposed method was tested on a variety of microscopy volumes collected from multiple regions of organ tissues. The execution time and robustness analyses show that our method is practical and effective.

cell biology↗