Biology subjects

Xu, Z.

Publications and source records attributed to Xu, Z..

At least 487 records · Page 27Linked to original sources

bioRxiv2020

Arabidopsis ERF4 and MYB52 Transcription Factors Play Antagonistic Roles in Regulating Homogalacturonan De-methylesterification in Seed Coat Mucilage

The Arabidopsis (Arabidopsis thaliana) seed coat mucilage is a specialized cell wall with pectin as its major component. Pectin is synthesized in the Golgi apparatus with homogalacturonan fully methylesterified, but it must undergo de-methylesterification by pectin methylesterase (PME) after being secreted into the cell wall. This reaction is critical for pectin maturation, but the mechanisms of its transcriptional regulation remain largely unknown. Here, we show that the Arabidopsis ERF4 transcription factor positively regulates pectin de-methylesterification during seed development and directly suppresses the expression of PME INHIBITOR13 (PMEI13), 14, 15 and SUBTILISIN-LIKE SERINE PROTEASE 1.7 (SBT1.7). The erf4 mutant seeds showed repartitioning of mucilage between soluble and adherent layers as a result of decreased PME activity and increased degree of pectin methylesterification. ERF4 physically associates with and antagonizes MYB52 in activating PMEI6, 14 and SBT1.7 and MYB52 also antagonizes ERF4 activity in the regulation of downstream targets. Gene expression studies revealed that ERF4 and MYB52 have opposite effects on pectin de-methylesterification. Genetic analysis indicated that the erf4-2 myb52 double mutant seeds show mucilage phenotype similar to wild-type. Taken together, this study demonstrates that ERF4 and MYB52 antagonize each others activity to maintain the appropriate degree of pectin methylesterification, expanding our understanding of how pectin de-methylesterification is fine-tuned by the ERF4-MYB52 transcriptional complex in the seed mucilage. One-sentence summaryArabidopsis ERF4 and MYB52 transcription factors interact and play antagonistic roles in regulating homogalacturonan de-methylesterification related genes in the seed coat mucilage.

plant biology↗

bioRxiv2020

A single nonsynonymous mutation on gene encoding E protein of Zika virus leads to increased neurovirulence in vivo

Zika virus can infect a wide range of tissues including the developmental brain of human fetuses, causing from mild to severe clinical diseases. Whether its genetic characteristics impacts on viral pathogenesis is incompletely understood. We have obtained viral variants through serially passage of a clinical Zika virus isolate (SW01) in neonatal mice in vivo and found some of which exhibited markedly increased virulence and neurotropism. By deep sequencing analysis, the more pathogenic viral variants were found to contain four dominant nonsynonymous nucleotide mutations on genes encoding E and NS2A proteins. Further investigation using molecularly cloned viruses revealed that a single 67D (Aspatic acid) to N (Asparagine) substitution on E protein is sufficient to confer the increased virulence and neurotropism. These findings provide new insight into Zika virus pathogenesis and suggest novel targets for the development of therapeutics. Author SummaryRecent large outbreaks of Zika virus infection worldwide have revealed an association between the viral infection and increased cases of specific neurological problems including Congenital Zika Syndrome (including microcephaly) and adult Guillain-Barre Syndrome. However, the determinants of the increased neurovirulence of Zika virus remain uncertain. One hypothesis is that some unique changes across the Zika viral genome have led to the occurrence of these neurological diseases. To test this hypothesis, we continuously propagated a clinical isolate of contemporary Zika virus (SW01) in neonatal mice brain for 11 times to obtain an mouse central nervous system (CNS) adapted Zika virus (MA-SW01) that showed significantly increased neurovirulence in vivo. We then discovered that a single G to A nucleotide substitution at the 1069 site of Zika virus open reading frame leading to a D (aspartic acid) to N (asparagine) in viral Envelope protein is responsible for the increased neurovirulence. These findings improve our understanding of the neurological pathogenesis of Zika virus and provide clues for the development of antiviral strategy.

microbiology↗

bioRxiv2020

BREM-SC: A Bayesian Random Effects Mixture Model for Joint Clustering Single Cell Multi-omics Data

Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10X Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.

genomics↗

bioRxiv2020

Single cell atlas of trisomy 21 cerebral cortex

Down syndrome (DS) is one of the most common human birth defects caused by trisomy 21 (T21), leading to a variety of cognitive impairments. The cellular composition of human brain has been explored using single cell RNA sequencing in both physiological and pathological conditions. However, the cellular heterogeneity of human brain with chromosome aneuploidy is largely unknown. Here, we profiled the transcriptome of 36046 cells in cerebral cortex of T21 human fetus, covering frontal lobe, parietal lobe, occipital lobe and temporal lobe. Intriguingly, we detected several genes positively associated with neurons maturation was dysregulated in T21 frontal cortex (HIC2, POU2F2, ZGLP1 and FOXK1). To share, explore and utilized the data resources of T21 cerebral cortex, we developed a comprehensive platform named T21atlas, composing of two functional modules (T21cluster and T21talk). Overall, our study provides, as far as we know, the first single cell atlas for T21 cerebral cortex, which could promote our understanding of the molecular mechanism of DS at an unprecedented resolution and could potentially facilitate the development of novel clinical therapeutics against T21.

neuroscience↗

bioRxiv2019

Developing a DeSipher method for conformational change detection of membrane receptor complex at residue resolution

While one dimensional 1H nuclear magnetic resonance (1D 1H-NMR) spectroscopy is one of the most important and convenient method for measuring conformation change in biomacromolecules, characterization of protein dynamics in large membrane protein complexes by 1D 1H-NMR remains challenging, due to the difficulty of spectra assignment, low signal-to-noise ratio (S/N) and the need for large amount of protein. Here we report the site-specific incorporation of 4-trimethylsilyl phenylalanine (TMSiPhe) into proteins, through genetic code expansion in Escherichia coli cells, and the measurement of multiple conformational states in membrane protein complex by 1D 1H-NMR. The unique up-field 1H-NMR chemical shift of TMSiPhe, highly efficient and specific incorporation of TMSiPhe enabled facile assignment of the TMSiPhe 1H-NMR signal, and characterization of multiple conformational state in a 150 kilodalton (kD) membrane protein complex, using only 5 M of protein and 20 min spectra accumulation time. This highly efficient and convenient methods should be broadly applicable for the investigation of dynamic conformation change of protein complexes.

biochemistry↗

bioRxiv2019

AutoGenome: An AutoML Tool for Genomic Research

Deep learning have made great successes in traditional fields like computer vision (CV), natural language processing (NLP) and speech processing. Those achievements greatly inspire researchers in genomic study and make deep learning in genomics a very hot topic. Convolutional neural network (CNN) and recurrent neural network (RNN) are frequently used for genomic sequence prediction problems; multiple layer perception (MLP) and auto-encoders (AE) are frequently used for genomic profiling data like RNA expression data and gene mutation data. Here, we introduce a new neural network architecture, named residual fully-connected neural network (RFCN) and demonstrate its advantage for modeling genomic profiling data. We further incorporate AutoML algorithms and implement AutoGenome, an end-to-end automated genomic deep learning framework. By utilizing the proposed RFCN architectures, automatic hyper-parameter search and neural architecture search algorithms, AutoGenome can train high-performance deep learning models for various kinds of genomic profiling data automatically. To make researchers better understand the trained models, AutoGenome can assess the feature importance and export the most important features for supervised learning tasks, and the representative latent vectors for unsupervised learning tasks. We envision AutoGenome to become a popular tool in genomic studies.

genomics↗

bioRxiv2019

Multi-Region Brain Stimulation Optimization Using Transcranial Direct Current Stimulation

Transcranial direct current stimulation (tDCS) is a type of noninvasive transcranial electrical brain stimulation. By optimizing the current distribution of each electrode on the scalp, the stimulation can be guided to a target brain region using a tDCS dense electrode array system. However, previous studies have yielded simple results using optimization schemes in single target stimulation cases. The detailed parameter settings for each optimization scheme and the associated simulation results have not been comprehensively assessed. In this study, we investigated parameter settings of optimization schemes in detail in both single target and multi-target cases. Two optimization schemes, minimum least squares (MLS) and maximum electrical field strength (ME), were examined in this study. MLS minimizes the squared errors between the expected electrical field and the estimated electrical field, whereas ME maximizes the electrical field strength in the target region. We constructed a five layer finite-element head model with 64 electrodes placed on the scalp according to the EEG 10/10 system for simulation. We evaluated the effects of stimulation using these two schemes under three conditions, 1) single target stimulation, 2) multi-target stimulation, and 3) multi-target stimulation under specific task activation, which shown that directly using MLS and ME scheme in multi-target stimulation case may lead to a wrong result. We also reported the improved results fixed by our proposed weighted MLS and weighted ME schemes which take detailed parameter settings into consideration. Our results indicate that the parameter settings in each optimization scheme greatly affected the final stimulation results, especially in the case of multi-target stimulation, and thus, indicate that the parameter settings of each optimization scheme should be carefully considered according to the expected stimulation mode. Our results also suggest that, by calculating the parameters through our proposed methods, the weighted ME and weighted MLS scheme can precisely distribute energy into each target brain region.

bioengineering↗

bioRxiv2019

Robust Hi-C chromatin loop maps in human neurogenesis and brain tissues at high-resolution

Genome-wide mapping of chromatin interactions at high resolution remains experimentally and computationally challenging. Here we used a low-input \"easy Hi-C\" (eHi-C) protocol to map the 3D genome architecture in neurogenesis and brain tissues, and also developed an improved Hi-C bias-correction pipeline (HiCorr) enabling better identification of enhancer loops or aggregates at sub-TAD level. We compared ultra-deep 3D genome maps from 10 human tissue- or cell types, with a focus on stem cells and neural development. We found several large loci in skin-derived human iPSC lines showing recurrent 3D compartmental memory of somatic heterochromatin. Chromatin loop interactions, but not genome compartments, are hallmarks of neural differentiation. Interestingly, we observed many cell type- or differentiation-specific enhancer aggregates spanning large neighborhoods, supporting a phase-separation mechanism that stabilizes enhancer contacts during development. Finally, we demonstrated that chromatin loop outperforms eQTL in explaining neurological GWAS results, revealing a unique value of high-resolution 3D genome maps in elucidating the disease etiology.\n\nHighlightsO_LILow input \"easy Hi-C\" protocol compatible with 50-100K cells\nC_LIO_LIImproved Hi-C bias correction allows direct observation and accurate identification of sub-TAD chromatin loops and enhancer aggregates\nC_LIO_LIRecurrent architectural memory of somatic heterochromatin at compartment level in skin-derived hiPSCs\nC_LIO_LIChromatin loop, but not genome compartment, marks neural differentiation\nC_LIO_LIChromatin loop outperforms eQTL in defining brain GWAS target genes\nC_LI

genomics↗

bioRxiv2019

NbCycB2 represses Nbwo activity via a negative feedback loop in the tobacco trichome developmemt

The wo protein and its downstream gene, SlCycB2 have been demonstrated to regulate the trichome development in tomato. It was shown that only gain-of-function mutant form of wo, Wov (wo woolly motif mutant allele) could induce the increase of trichome density. However, it is still unclear the relationships between wo, Wov and SlCycB2 in trichome regulation. In this study, we demonstrated Nbwo (NbWov) directly regulated the expressions NbCycB2 by binding to the promoter of NbCycB2 and its genomic sequences. As a feedback regulation, NbCycB2 negatively regulates the trichome formation by repressing Nbwo activity at protein level. We further found that the mutations of Nbwo woolly motif could prevent repression of NbWov by NbCycB2, which results in the significant increase of active Nbwo proteins, trichome density and branches. Our results revealed a novel reciprocal mechanism between NbCycB2 and Nbwo during the trichome formation in Nicotiana benthamiana.\n\nHighlightNbCycB2 is specifically expressed in trichomes of Nicotiana benthamiana and represses the Nbwo activity via a negative feedback loop in tobacco trichome developmemt.

plant biology↗

bioRxiv2019

Mutational signatures in upper tract urothelial carcinoma define etiologically distinct subtypes with prognostic relevance

Parts of East Asia have a very high upper tract urothelial carcinoma (UTUC) prevalence, and an etiological link between the medicinal use of herbs containing aristolochic acid (AA) and UTUC has been established. The mutational signature of AA, which is characterized by a particular pattern of A:T to T:A transversions, can be detected by genome sequencing. Thus, integrating mutational signatures analysis with clinicopathological data may be a crucial step toward personalized treatment strategies for the disease. Therefore, we performed whole-genome sequencing (WGS) of 90 UTUC patients from China. Mutational signature analysis via nonnegative matrix factorization method defined three etiologically distinct subtypes with prognostic relevance: (i) AA, the typical AA mutational signature characterized by signature 22, had the highest tumor mutation burden, the best clinical outcomes; (ii) Age, an age-related group featured by signatures 1 and 5, had the lowest weighted genome instability index score, the worst clinical outcomes; and (iii) DSB, signature with deficiencies in DNA double strand break-repair featured by signatures 3, the intermediate clinical outcomes. Additionally, the distinct AA subtype was associated with AA exposure, the highest number of predicted neoantigens and heavier lymphocytes infiltrating. Thus, it may be good candidate for immune checkpoint blockade therapy. Notably, we showed AA-mutational signature was also identified in histologically \"normal\" urothelial cells. Thus, non-invasive urine test based on the AA-mutational signature could take advantage of this \"field effect\" to screen individuals at increased risk of recurrence due to exposure to herbal remedies containing the carcinogen AA. Collectively, the findings here may accelerate the development of novel prognostic markers and personalized therapeutic approaches for UTUC patients in China.

genomics↗

bioRxiv2019

Using Machine Learning and Gene Nonhomology Features to Predict Gene Ontology

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

bioinformatics↗

bioRxiv2019

Substrate Transport is Mediated not only by P-glycoprotein but also by Lipid Penetration

In association with large-scale conformational changes, the members of the ATP-binding cassette (ABC) transporter superfamily such as P-glycoprotein (P-gp) pump endogenous cytotoxic substances and exogenous drugs out of cells. Here, a series of nonequilibrium-driven molecular dynamics (MD) simulations are sophisticatedly combined to provide a generally effective access to quantitatively investigate such a complex biological process that has been posing a great challenge for experiments and computational simulations. Both common features and unique characteristics of multiple ligands (substrates or inhibitors) that are recognized by P-gps from mouse and human species are quantitatively explored, providing additional insights into experimentally suggested ligand transport pathways and summarizing the important roles of not only different P-gps but also lipids in regulating ligand transport. These findings reveal the molecular mechanism underlying the transport of ligands by P-gps from different species and emphasize the consideration of lipid effects on the future design of effective P-gp inhibitors.

biophysics↗

bioRxiv2019

Simulated Plant Images Improve Maize Leaf Counting Accuracy

Automatically scoring plant traits using a combination of imaging and deep learning holds promise to accelerate data collection, scientific inquiry, and breeding progress. However, applications of this approach are currently held back by the availability of large and suitably annotated training datasets. Early training datasets targeted arabidopsis or tobacco. The morphology of these plants quite different from that of grass species like maize. Two sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait. Convolutional neural networks (CNNs) trained on entirely synthetic data provided predictive power for scoring leaf number in real-world images. This power was less than CNNs trained with equal numbers of real-world images, however, in some cases CNNs trained with larger numbers of synthetic images outperformed CNNs trained with smaller numbers of real-world images. When real-world training images were scarce, augmenting real-world training data with synthetic data provided improved prediction accuracy. Quantifying leaf number over time can provide insight into plant growth rates and stress responses, and can help to parameterize crop growth models. The approaches and annotated training data described here may help future efforts to develop accurate leaf counting algorithms for maize.

plant biology↗

bioRxiv2019

Cohesin promotes interactions across domain boundaries to regulate gene expression

The mammalian genome can be segmented into thousands of topologically associated domains (TADs) based on chromosome conformation capture studies, such as Hi-C. TADs have been proposed to act as insulated neighborhoods, spatially sequestering and insulating the enclosed genes and regulatory elements through chromatin looping and self-association. Recent results indicate that inter-TAD interactions can also occur, suggesting boundaries may be semi-permissible. However, the nature, extent, and function, if any, of these inter-TAD interactions remains unclear. Here, we combine super-and high-resolution microscopy with Oligopaint technology to precisely quantify the interaction frequency within and between neighboring domains in human cells. We find that intermingling across domain boundaries is a widespread feature of the human genome, with varying levels of interactions across different loci that correlate with their differing boundary strengths by Hi-C. Moreover, we find that cohesin depletion, which is known to abolish TADs at the population-average level, does not induce ectopic interactions but instead reduces both intra- and inter-domain interactions to a similar extent. Reduced chromatin intermixing due to cohesin loss affects domain incorporation and transcriptional bursting frequencies of genes close to architectural boundaries, potentially explaining the gene expression changes observed in the cohesinopathy Cornelia de Lange syndrome. Together, our results provide a mechanistic explanation for stochastic domain intermingling, arguing that cohesin partially bypasses boundaries to promote alternating incorporation of boundary-proximal genes into neighboring regulatory domains.

cell biology↗

bioRxiv2019

A Novel L-Serine Exporter and Its Positive Regulator in Corynebacterium glutamicum

Exporters play an essential role in the fermentative production of amino acids. In Corynebacterium glutamicum, ThrE, which can export O_SCPCAPLC_SCPCAP-threonine and O_SCPCAPLC_SCPCAP-serine, is the only identified O_SCPCAPLC_SCPCAP-serine exporter so far. In this study, a novel O_SCPCAPLC_SCPCAP-serine exporter NCgl0580 was identified and characterized in C. glutamicum {Delta}SSAAI (SSAAI), and named as SerE (encoded by serE). Deletion of serE in SSAAI led to a 56.5% decrease in O_SCPCAPLC_SCPCAP-serine titer, whereas overexpression of serE compensated for the lack of serE with respect to O_SCPCAPLC_SCPCAP-serine titer. A fusion protein with SerE and enhanced green fluorescent protein (EGFP) was constructed to confirm that SerE localized at the plasma membrane. The function of SerE was studied by peptide feeding approaches, and the results showed that SerE is a novel exporter for O_SCPCAPLC_SCPCAP-serine and O_SCPCAPLC_SCPCAP-threonine in C. glutamicum. Subsequently, the interaction of a known O_SCPCAPLC_SCPCAP-serine exporter ThrE and SerE was studied, and the results suggested that SerE is more important than ThrE in O_SCPCAPLC_SCPCAP-serine export in SSAAI. Probe plasmid and electrophoretic mobility shift assays (EMSA) revealed NCgl0581 as the transcriptional regulator of SerE. Comparative transcriptomics between SSAAI and the NCgl0581 deletion strain showed that NCgl0581 regulated the transcription of 115 genes in C. glutamicum, among which the transcriptional level of NCgl0580 decreased 280-fold in a NCgl0581 deletion strain, indicating that NCgl0581 is a positive regulator of SerE. Thus, this study provides a novel target for O_SCPCAPLC_SCPCAP-serine and O_SCPCAPLC_SCPCAP-threonine export engineering as well as a novel global transcriptional regulator NCgl0581 in C. glutamicum.\n\nImportanceExporters are gaining increasing attention for improving industrial production of amino acids. This study identified a novel exporter NCgl0580 for O_SCPCAPLC_SCPCAP-serine and O_SCPCAPLC_SCPCAP-threonine in C. glutamicum, and its positive regulator (NCgl0581), which was shown to be a novel global transcriptional regulator in C. glutamicum. This study provides a new target for engineering efflux of O_SCPCAPLC_SCPCAP-serine and O_SCPCAPLC_SCPCAP-threonine, expands the exporter and transcriptional regulator family, and enriches our understanding of amino acid transport system in C. glutamicum.

bioengineering↗

bioRxiv2019

Epigenome-wide analysis uncovers a blood-based DNA methylation biomarker of lifetime cannabis use.

Cannabis use is highly prevalent and is associated with adverse and beneficial effects. To better understand the full spectrum of health consequences, biomarkers that accurately classify cannabis use are needed. DNA methylation (DNAm) is an excellent candidate, yet no blood-based epigenome-wide association studies (EWAS) in humans exist. We conducted an EWAS of lifetime cannabis use (ever vs. never) using blood-based DNAm data from a case-cohort study within Sister Study, a prospective cohort of women at risk of developing breast cancer (Discovery N=1,730 [855 ever users]; Replication N=853 [392 ever users]). We identified and replicated an association with lifetime cannabis use at cg15973234 (CEMIP): combined P=3.3x10-8. We found no overlap between published blood-based cis-meQTLs of cg15973234 and reported lifetime cannabis use-associated SNPs (P<0.05), suggesting that the observed DNAm difference was driven by cannabis exposure. We also developed a multi-CpG classifier of lifetime cannabis use using penalized regression of top EWAS CpGs. The resulting 49-CpG classifier produced an area under the curve (AUC)=0.74 (95% confidence interval [0.72, 0.76], P=2.00x10-5) in the discovery sample and AUC=0.54 ([0.51, 0.57], P=4.64x10-2) in the replication sample. Our EWAS findings provide evidence that blood-based DNAm is associated with lifetime cannabis use.

genomics↗

bioRxiv2019

Sing cell RNA-seq reveals cellular diversity and developmental characteristics of human infant retina

Retina, located in the innermost layer of the eye of human, holds the decisive role in visual perception. Dissecting the heterogeneity of retina is essential for understanding the mechanism of vision formation and even the development of central nervous system (CNS). Here, we performed single cell RNA-seq, analyzed 57,832 cells from human infant donors, resulting in 20 distinct clusters representing major cell types in retina: rod photoreceptors, cone photoreceptors, bipolar cells, horizontal cells, amacrine cells, Muller glia cells and microglia. We next constructed extensive networks of intercellular communication and identified ligand-receptor interactions playing crucial roles in regulating neural cell development and immune homeostasis in retina. Though re-clustering, we identified known subtypes in cone PRs and additional unreported subpopulations and corresponding markers in rod PRs as well as bipolar cells. Additionally, we linked inherited retinal disease to certain cell subtypes or subpopulations through enrichment analysis. Intriguingly, we found that status and functions of photoreceptors changed drastically between early and late retina. Overall, our study offers the first retinal cell atlas in human infants, dissecting the heterogeneity of retina and identifying the key molecules in the developmental process, which provides an important resource that will pave the way for retina development mechanism research and regenerative medicine concerning retinal biology.

cell biology↗

bioRxiv2019

Single cell RNA sequencing reveals cellular diversity of trisomy 21 retina

Retina is a crucial tissue for the capturing and processing of light stimulus. Characterization of the retina at single cell level is essential for the understanding of its biological functions. A variety of abnormalities in terms of morphology and function were reported in T21 retina. To evaluate the effects of chromosome aneuploidy on retina development, we characterized single cell transcriptional profiles of a T21 fetus and performed comprehensive bioinformatic analyses. Our data revealed the diversity and heterogeneity of cellular compositions in T21 retina. In total, we identified seven major cell types, and detected several subtypes within each cell type, followed by the detection of corresponding molecular markers including previously reported ones and a series of novel markers. Our analyses identified extensive communication networks between distinct cellular types, among which a few ligand-receptor interactions were associated with the development of retina and immunoregulatory interactions. Taken together, our data provided the first single cell transcriptome profile for human T21 retina which facilitates our understanding on the dosage effects of chromosome 21 on the development of retina.

cell biology↗