Biology subjects

Gao, R.

Publications and source records attributed to Gao, R..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Tiled PCR amplification-based Whole Genome Sequencing and Phylogenetic Classification Accelerate the Implementation of Respiratory Syncytial Virus Genomic surveillance in Canada as a Pilot Study

Whole genome sequencing (WGS) has emerged as a powerful tool to facilitate the study of existing and emerging infectious diseases. WGS-based genomic surveillance provides information on the genetic diversity and tracks the evolution of important viral pathogens including Respiratory Syncytial Virus (RSV). Development and implementation of robust tiled multiplex PCR amplification-based WGS assays will facilitate high-throughput RSV surveillance initiatives. In this study, we developed multiplex PCR assays for targeted enrichment of viral genomes using PrimalScheme (http://primal.zibraproject.org) to amplify over 97% of the genome in the majority of contemporaneous specimens tested. A pilot dataset comprising 52 RSVA and 37 RSVB genomes derived from Canadian clinical specimens during the 2022-2023 respiratory virus season were used to perform phylogenetic analyses using both near complete genome and Glycoprotein (G) sequences. Overall, the RSV phylogenetic tree built with whole genomes showed identical lineage clusters as that compared to the G gene, but showed more confidence and discriminatory features within individual lineage. Moreover, availability of whole genomes enabled the identification of a broader range of mutations, for instance the identified S377N, K272M, S276N, S211N, S206I and S209Q in Canadian fusion proteins that could be potentially associated with effectiveness of vaccines or antiviral-based therapeutics. In conclusion, the tiled-PCR amplification assays described offer a more streamlined approach to facilitate high-throughput, high sensitivity of RSV WGS, which is capable of supporting enhanced genomic surveillance initiatives, as well as the more comprehensive genomic analyses required to inform public health strategies for the development and usage of vaccines and antiviral drugs. IMPORTANCEWe present assays to efficiently sequence genomes of the RSVA and RSVB. This enables researchers and public health agencies to acquire high-quality genomic data using rapid and cost-effective approaches. Genomic data based comparative analysis can be used to conduct surveillance and monitor circulating isolates for efficacy of vaccines and antiviral therapeutics.

genomics↗

bioRxiv2024

Decoding the Neural Dynamics of Headed Syntactic Structure Building

The brain builds hierarchical phrases during language comprehension; however, the representational details and dynamics of the phrase-building process remain underspecified. This study directly probes whether the neural code of a phrase involves reactivating the syntactic property of a key subcomponent (the "phrasal head"). To this end, we train a part-of-speech sliding-window neural decoder (verb vs. adverb) on EEG signals recorded while participants (N = 30) read sentences in a controlled experiment. The decoder reaches above-chance performance that is spatiotemporally consistent and generalizes to unseen data across sentence positions. Appling the decoder to held-out data yields predicted activation levels for the verbal "head" of a verb phrase at a distant non-head word (adverb); the critical adverb appeared either at the end of a verb phrase or at a sequentially and lexically matched position with no verb phrase boundary. There is stronger verb activation beginning at [~]600 milliseconds at the critical adverb when it appears at a verb phrase boundary; this effect is not modulated by the strength of conceptual association between the two subcomponents in the verb phrase nor does it reflect word predictability. Time-locked analyses additionally reveal a negativity waveform component and increased beta-delta inter-trial phase coherence, both previously linked to linguistic composition, in a similar time window. With a novel application of neural decoding, our findings delineate the temporal dynamics by which the brain encodes phrasal representations by, in part, reactivating the representation of key subcomponents. We thus establish a link between cognitive accounts of phrase structure representations and electrophysiological dynamics.

neuroscience↗

bioRxiv2024

Evaluating and Comparing Measures of Aperiodic Neural Activity

Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or scale-free activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain. There is a lack of clear understanding of how these different measures relate to each other and to what extent they reflect the same or different properties of the data, which makes it difficult to synthesize results across approaches and complicates our overall understanding of the properties, biological significance, and demographic, clinical, and behavioral correlates of aperiodic neural activity. To address this problem, in this project we systematically survey the different approaches for measuring aperiodic neural activity, starting with an automated literature analysis to curate a collection of the most common methods. We then evaluate and compare these methods, using statistically representative time series simulations. In doing so, we establish consistent relationships between the measures, showing that much of what they capture reflects shared variance - though with some notable idiosyncrasies. Broadly, frequency domain methods are more specific to aperiodic features of the data, whereas time domain measures are more impacted by oscillatory activity. We extend this analysis by applying the measures to a series of empirical EEG and iEEG datasets, replicating the simulation results. We conclude by summarizing the relationships between the multiple methods, emphasizing opportunities for re-examining previous findings and for future work.

neuroscience↗

bioRxiv2024

Deep inverse modeling reveals dynamic-dependent invariances in neural circuit mechanisms

Neural population dynamics are shaped by many cellular, synaptic, and network properties. Not only is it important to understand how coordinated changes in circuit parameters alter neural activity, but also when dynamics are unaffected by--or invariant to--such changes. Computational modeling has revealed invariances in single neurons and small circuits that are thought to reflect their robustness against variability and perturbations. However, generalizing these insights to larger circuits in cortex and other brain areas remains challenging. A key bottleneck lies in inverse modeling of neural circuits with spiking network models, i.e., identifying parameter configurations that quantitatively match dynamics observed in neural recordings. Here, we present Automated Model Inference from Neural Dynamics (AutoMIND) for efficient discovery of invariant circuit model configurations. AutoMIND leverages a mechanistic model with adaptive spiking neurons and clustered connectivity, which displays a rich variety of spatiotemporal dynamics. Probabilistic deep generative models--trained on network simulations only--then returns many parameter configurations consistent with a given target observation of neural activity. Applied to several datasets, AutoMIND discovers circuit models of synchronous network bursting in human brain organoids across early development, as well as models capturing complex frequency profiles of Neuropixels recordings in mouse hippocampus and cortex. In each case, we obtain hundreds of configurations that compose a (nonlinear) parameter subspace in which population dynamics remain unchanged. Surprisingly, global and local geometries of the invariant subspace are not fixed, but differ for different dynamics. Together, our results shed light on dynamic-dependent invariances of circuit parameters underlying diverse population dynamics, while demonstrating the flexibility of AutoMIND for inverse modeling of neural circuits.

neuroscience↗

bioRxiv2024

Nanoscale volumetric fluorescence imaging via photochemical sectioning

Optical nanoscopy of intact biological specimens has been transformed by recent advancements in hydrogel-based tissue clearing and expansion, enabling the imaging of cellular and subcellular structures with molecular contrast. However, existing high-resolution fluorescence microscopes have limited imaging depth, which prevents the study of whole-mount specimens without physical sectioning. To address this challenge, we developed "photochemical sectioning," a spatially precise, light-based sample sectioning process. By combining photochemical sectioning with volumetric lattice light-sheet imaging and petabyte-scale computation, we imaged and reconstructed axons and myelination sheaths across entire mouse olfactory bulbs at nanoscale resolution. An olfactory-bulb-wide analysis of myelinated and unmyelinated axons revealed distinctive patterns of axon degeneration and de-/dysmyelination in the neurodegenerative mouse, highlighting the potential for peta- to exabyte-scale super-resolution studies using this approach.

neuroscience↗

bioRxiv2024

Cost-effective DNA storage with DNA movable type

In the face of exponential data growth, DNA-based storage offers a promising solution for preserving big-data. However, most existing DNA storage methods, akin to traditional block printing, require costly chemical synthesis for each individual data file, adopting a sequential, one-time-use synthesis approach. To overcome these limitations, we introduce a novel, cost-effective "DNA-Movable-Type Storage" system, inspired by movable type printing. This system utilizes pre-fabricated DNA movable types-short, double-stranded DNA oligonucleotides encoding specific payload, address, and checksum data. These DNA-MTs are enzymatically ligated/assembled into cohesive sequences, termed "DNA movable type blocks", streamlining the assembly process with the automated BISHENG-1 DNA-MT inkjet printer. Using BISHENG-1, we successfully printed, assembled, stored and accurately retrieved 43.7 KB of data files in diverse formats (text, image, audio, and video) in vitro and in vivo, using only 350 DNA-MTs. Notably, each DNA-MT, synthesized once (2 OD), can be used up to 10,000 times, reducing costs to 122 $/MB-outperforming existing DNA storage methods. This innovation circumvents the need to synthesize entire DNA sequences encoding files from scratch, offering significant cost and efficiency advantages. Furthermore, it has considerable untapped potential to advance a robust DNA storage system, better meeting the extensive data storage demands of the big-data era.

bioinformatics↗

bioRxiv2024

Transcriptomic study of testicular hypoxia adaptation in Tibetan sheep

The Tibetan sheep is a typical hypoxia-tolerant mammal, which lives on the plateau, an altitude of between 2,500 and 5,000 meters above sea level, the study of its hypoxic adaptation mechanism provides reference for exploring the hypoxic adaptation mechanism of other animals. To grope for the genetic mechanism of adaptation to hypoxic environment at the transcriptional level in Tibetan sheep testicular tissue, and to identify candidate genes and key pathways related to sheep adaptation. Histological observation of testicular tissues from two sheep breeds was carried out using hematoxylin-eosin (HE) conventional staining. A total of 277 differentially expressed genes (DEGs) were authenticated at high altitude Tibetan sheep (ZYH) and low altitude Tibetan sheep (ZYM) by RNA sequencing technology (RNA-Seq), which included 165 up-regulated genes and 112 down-regulated genes. Functional analyses revealed several terms and pathways, such as were closely related with testis adaptation to plateau. Several genes (including GGT5, AGTR2, EDN1, LPAR3, CYP2C19, IGFBP3, APOC3 and PKC1) were remarkably enriched in several pathways and terms, which may impact the Plateau adaptability of sheep by adjusting its reproductive activity and sexual maturation, and protecting Sertoli cells, various spermatocytes and spermatogenesis processes. The results make a reasonable case for a better understanding of the molecular mechanisms of adaptation to altitude in sheep.

genomics↗

bioRxiv2024

Defining cellular determinants of resistance to PD-1 pathway blockade in non-small-cell lung cancer

Despite sustained clinical responses to immune-checkpoint blockade (ICB) therapies in non-small-cell lung cancer (NSCLC), the majority of patients derive no clinical benefits, and the cellular and molecular underpinnings of such resistance remain incompletely understood. To identify cell types that may influence immunotherapy responses, we first integrated newly generated and previously published single-cell RNA sequencing data from 110 treatment-naive patients with NSCLC. Among tumor-resident cell types, we identified MMP1+ cancer-associated fibroblasts (CAFs), which were inversely correlated with the level of tumor-reactive T cells--a key determinant of response to ICB. Further single-cell analysis for newly collected 21 tumor samples from NSCLC patients treated with anti-PD-1/PD-L1 agents revealed that MMP1+ fibroblasts were indeed enriched in treatment-refractory patients, and this observation was also validated in an independent dataset of bulk RNA sequencing from 344 NSCLC patients treated with PD-L1 agents. Examination of the spatial architecture showed that MMP1+ fibroblasts were located at the tumor-stroma boundary, forming a single-cell layer that encircled the cancer cell aggregates, and we hence defined MMP1+ fibroblasts as tumor-stroma boundary (tsb)CAFs. Such tsbCAFs likely promote resistance to ICB by functioning as a physical barrier that prevents tumor-reactive T cells from recognizing and killing cancer cells. Our study provides a new framework to identify cellular underpinnings of resistance to ICB and suggests new strategies to overcome ICB resistance. Highlights{lozenge} Identification and characterization of MMP1+ fibroblasts in lung cancer. {lozenge}Single-cell meta-analysis reveals cell populations impeding the accumulation of tumor-reactive T cells. {lozenge}MMP1+ fibroblasts correlate with the low infiltration of tumor-reactive T cells and the resistance to anti-PD-1/PD-L1 treatment. {lozenge}MMP1+ fibroblasts appear to form a space barrier between malignant and T cells.

immunology↗

bioRxiv2024

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

Computational pathology, utilizing whole slide image (WSI) for pathological diagnosis, has advanced the development of intelligent healthcare. However, the scarcity of annotated data and histological differences hinder the general application of existing methods. Extensive histopathological data and the robustness of self-supervised models in small-scale data demonstrate promising prospects for developing foundation pathology models. Due to the need for deployment, lightweight foundation models also need to be developed. In this work, we propose the BEPH (BEiT-based model Pre-training on Histopathological images), a general lightweight foundation model that leverages self-supervised learning to learn meaningful representations from 11 million unlabeled histopathological images. These representations are then efficiently adapted to various tasks, including 2 cancer patch-level recognition tasks, 3 cancer WSI-level classification tasks, and 6 cancer subtypes survival prediction tasks. Experimental results demonstrate that our model consistently outperforms several comparative models with similar parameters, even with limited training data reduced to 50%. Especially when the downstream structure is the same, the model can improve ResNet and DINO by up to a maximum increase of 8.8% and 7.2% (WSI level classification), and 6.44% and 3.28% on average (survival prediction), respectively. Therefore, BEPH offers a universal solution to enhance model performance, reduce the burden of expert annotations, and enable widespread clinical applications of artificial intelligence. The code and models can be obtained at https://github.com/Zhcyoung/BEPH. And currently, online fine-tuning of WSI classification tasks is available for use on http://yulab-sjtu.natapp1.cc/BEPH.

pathology↗

bioRxiv2024

Stabilizing mammalian RNA thermometer confers neuroprotection in subarachnoid hemorrhage

Mammals tightly regulate their core body temperature, yet how cells sense and respond to small temperature changes at the molecular level remains incompletely understood. Here, we discover a significant enrichment of RNA G-quadruplex (rG4) motifs around splice sites of cold-repressed exons. These thermosensing RNA structures, when stabilized, mask splice sites, reducing exon inclusion. Focusing on cold-induced neuroprotective RBM3, we demonstrate that rG4s near splice sites of a cold-repressed poison exon are stabilized at low temperatures, leading to exon exclusion. This enables evasion of nonsense-mediated decay, increasing RBM3 expression at cold. Additionally, increasing intracellular potassium concentration stabilizes rG4s and enhances RBM3 expression, leading to RBM3-dependent neuroprotection in a mouse model of subarachnoid hemorrhage. Our findings unveil a mechanism how mammalian RNAs directly sense temperature and potassium perturbations, integrating them into gene expression programs. This opens new avenues for treating diseases arising from splicing defects and disorders benefiting from therapeutic hypothermia. HighlightsO_LIrG4s are enriched near splice sites of cassette exons repressed upon cold shock C_LIO_LIrG4s act as RNA thermometers in mammals by controlling accessibility of splice sites C_LIO_LIrG4 stability mediates temperature-dependent RBM3 expression ex vivo and in vivo C_LIO_LIStabilizing rG4s in RBM3 exon 3a reduces brain damage in subarachnoid hemorrhage C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/594628v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@e1d625org.highwire.dtl.DTLVardef@b22d21org.highwire.dtl.DTLVardef@1b0faeaorg.highwire.dtl.DTLVardef@294ac1_HPS_FORMAT_FIGEXP M_FIG C_FIG Here is a refined model of RNA G-quadruplexes acting as evolutionarily conserved thermo- and potassium sensors, modulating alternative splicing in mammals. RNA G-quadruplexes (rG4s) function as reversible temperature sensors, impacting alternative splicing dynamics. In low temperatures or high potassium conditions, rG4s can mask surrounding splice sites, rendering these sites inaccessible, thereby promoting exon skipping. Conversely, at high temperatures or under low potassium conditions, rG4s become destabilized, allowing splice sites to be exposed, and facilitating efficient exon inclusion. RBM3, a well-known cold-induced protein with neuroprotective functions, harbors a poison exon with rG4s around splice sites, that, upon inclusion, triggers NMD (non-sense mediated decay) of the RBM3 mRNA. Under low temperatures or high potassium conditions, rG4s shield the splice sites, leading to poison exon skipping and increased RBM3 expression. Stabilization of these rG4s through increased K+ promotes poison exon skipping, enabling escape from NMD, and ultimately elevating RBM3 expression. Notably, 4-AP, a clinically used pan voltage-gated potassium channel blocker, protects against neuronal damage in a subarachnoid hemorrhage mouse model in an RBM-dependent manner. (ISS: intronic splicing silencer, ESE: exon splicing enhancer, ACA: anterior cerebral artery, MCA: middle cerebral artery, PPA: pterygopalatine artery, ICA: internal carotid artery, ECA: external carotid artery, CCA: common carotid artery)

molecular biology↗

bioRxiv2024

A Conserved Inhibitory Interdomain Interaction Regulates DNA-binding Activities of Hybrid Two-component Systems in Bacteroides

Hybrid two-component systems (HTCSs) comprise a major class of transcription regulators of polysaccharide utilization genes in Bacteroides. Distinct from classical two-component systems in which signal transduction is carried out by intermolecular phosphotransfer between a histidine kinase (HK) and a cognate response regulator (RR), HTCSs contain the membrane sensor HK and the RR transcriptional regulator within a single polypeptide chain. Tethering the DNA-binding domain (DBD) of the RR with the dimeric HK domain in an HTCS could potentially promote dimerization of the DBDs and would thus require a mechanism to suppress DNA-binding activity in the absence of stimulus. Analysis of phosphorylation and DNA-binding activities of several HTCSs from Bacteroides thetaiotaomicron revealed a DBD-suppression mechanism in which an inhibitory interaction between the DBD and the phosphoryl group-accepting receiver domain (REC) decreases autophosphorylation rates of HTCS-RECs as well as represses DNA-binding activities in the absence of phosphorylation. Sequence analyses and structure predictions identified a highly conserved sequence motif correlated with a conserved inhibitory domain arrangement of REC and DBD. Presence of the motif, as in most HTCSs, or its absence, in a small subset of HTCSs, is likely predictive of two distinct regulatory mechanisms evolved for different glycans. Substitutions within the conserved motif relieve the inhibitory interaction and result in elevated DNA-binding activities in the absence of phosphorylation. Our data suggest a fundamental regulatory mechanism shared by most HTCSs to suppress DBD activities using a conserved inhibitory interdomain arrangement to overcome the challenge of the fused HK and RR components. ImportanceDifferent dietary and host-derived complex carbohydrates shape the gut microbial community and impact human health. In Bacteroides, the prevalent gut bacteria genus, utilization of these diverse carbohydrates relies on different gene clusters that are under sophisticated control by various signaling systems, including the hybrid two-component systems (HTCSs). We have uncovered a highly conserved regulatory mechanism in which the output DNA-binding activity of HTCSs is suppressed by interdomain interactions in the absence of stimulating phosphorylation. A consensus amino acid motif is found to correlate with the inhibitory interaction surface while deviations from the consensus can lead to constitutive activation. Understanding of such conserved HTCS features will be important to make regulatory predictions for individual systems as well as to engineer novel systems with substitutions in the consensus to explore the glycan regulation landscape in Bacteroides.

biochemistry↗

bioRxiv2024

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

Extracting the relationship between high-dimensional recordings of neural activity and complex behav- ior is a ubiquitous problem in systems neuroscience. Toward this goal, encoding and decoding models attempt to infer the conditional distribution of neural activity given behavior and vice versa, while dimensionality reduc- tion techniques aim to extract interpretable low-dimensional representations. Variational autoencoders (VAEs) are flexible deep-learning models commonly used to infer low-dimensional embeddings of neural or behavioral data. However, it is challenging for VAEs to accurately model arbitrary conditional distributions, such as those encountered in neural encoding and decoding, and even more so simultaneously. Here, we present a VAE-based approach for accurately calculating such conditional distributions. We validate our approach on a task with known ground truth and demonstrate the applicability to high-dimensional behavioral time series by retrieving the condi- tional distributions over masked body parts of walking flies. Finally, we probabilistically decode motor trajectories from neural population activity in a monkey reach task and query the same VAE for the encoding distribution of neural activity given behavior. Our approach provides a unifying perspective on joint dimensionality reduction and learning conditional distributions of neural and behavioral data, which will allow for scaling common analyses in neuroscience to todays high-dimensional multi-modal datasets.

neuroscience↗

bioRxiv2024

Axonal organelle buildup from loss of AP-4 complex function causes exacerbation of amyloid plaque pathology and gliosis in Alzheimers disease mouse model

Lysosomes and related precursor organelles robustly build up in swollen axons that surround amyloid plaques and disrupted axonal lysosome transport has been implicated in worsening Alzheimers pathology. Our prior studies have revealed that loss of Adaptor protein-4 (AP-4) complex function, linked primarily to Spastic Paraplegia (HSP), leads to a similar build of lysosomes in structures we term "AP-4 dystrophies". Surprisingly, these AP-4 dystrophies were also characterized by enrichment of components of APP processing machinery, {beta}-site cleaving enzyme 1 (BACE1) and Presenilin 2. Our studies examining whether the abnormal axonal lysosome build up resulting from AP-4 loss could lead to amyloidogenesis revealed that the loss of AP-4 complex function in an Alzheimers disease model resulted in a strong increase in size and abundance of amyloid plaques in the hippocampus and corpus callosum as well as increased microglial association with the plaques. Interestingly, we found a further increase in enrichment of the secretase, BACE1, in the axonal swellings of the plaques of Alzheimer model mice lacking AP-4 complex compared to those having normal AP-4 complex function, suggestive of increased amyloidogenic processing under this condition. Additionally, the exacerbation of plaque pathology was region-specific as it did not increase in the cortex. The burden of the AP-4 linked axonal dystrophies/AP-4 dystrophies was higher in the corpus callosum and hippocampus compared to the cortex, establishing the critical role of AP-4 -dependent axonal lysosome transport and maturation in regulating amyloidogenic amyloid precursor protein processing. Significance StatementA major pathological feature of Alzheimers disease is the accumulation of axonal lysosomes near sites of amyloid plaques. Lysosome accumulation is thought to contribute to amyloid production. In fact, a genetic perturbation that arrests lysosomes in axons exacerbates amyloid plaque pathology. The mechanisms that control axonal lysosome abundance as well the molecular composition of axonal endolysosomes that produce Abeta, however, are not fully understood. Axonal lysosome build-up is emerging as a common pathology in other neurodegenerative disorders such as Hereditary Spastic Paraplegia (HSP), but its relevance to amyloid production is unknown. We find that a model of HSP caused by loss of AP-4 adaptor complex lead to axonal lysosome buildup that differs in some of its content, but still contributes to amyloidogenesis. This demonstrates that different perturbations leading to changes in heterogeneous pool of axonal lysosomes can converge on a common pathology.

cell biology↗

bioRxiv2024

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value for the characterizing and understanding of tissue architecture. However, the inherent heterogeneity and varying spatial resolutions present challenges in the joint analysis of multi-modal SRT data. We introduce a multi-modal geometric deep learning method, named stMMR, to effectively integrate gene expression, spatial location and histological information for accurate identifying spatial domains from SRT data. stMMR uses graph convolutional networks (GCN) and self-attention module for deep embedding of features within unimodal and incorporates similarity contrastive learning for integrating features across modalities. Comprehensive benchmark analysis on various types of spatial data shows superior performance of stMMR in multiple analyses, including spatial domain identification, pseudo-spatiotemporal analysis, and domain-specific gene discovery. In chicken heart development, stMMR reconstruct the spatiotemporal lineage structures indicating accurate developmental sequence. In breast cancer and lung cancer, stMMR clearly delineated the tumor microenvironment and identified marker genes associated with diagnosis and prognosis. Overall, stMMR is capable of effectively utilizing the multi-modal information of various SRT data to explore and characterize tissue architectures of homeostasis, development and tumor.

bioinformatics↗

bioRxiv2023

Deconer: A comprehensive and systematic evaluation toolkit for reference-based cell type deconvolution algorithms using gene expression data

In recent years, computational methods for quantifying cell type proportions from transcription data have gained significant attention, particularly those reference-based methods which have demonstrated high accuracy. However, there is currently a lack of comprehensive evaluation and guidance for available reference-based deconvolution methods in cell proportion deconvolution analysis. In this study, we propose a comprehensive evaluation toolkit, called Deconer, specifically designed for reference-based deconvolution methods. Deconer provides various simulated and real gene expression datasets, including both bulk and single-cell sequencing data, and offers multiple visualization interfaces. By utilizing Deconer, we conducted systematic comparisons of 14 reference-based deconvolution methods from different perspectives, including method robustness, accuracy in deconvolving rare components, signature gene selection, and building external reference. We also performed an in-depth analysis of the application scenarios and challenges in cell proportion deconvolution methods. Finally, we provided constructive suggestions for users in selecting and developing cell proportion deconvolution algorithms. This work presents novel insights to researchers, assisting them in choosing appropriate toolkits, applying solutions in clinical contexts, and advancing the development of deconvolution tools tailored to gene expression data.

bioinformatics↗

bioRxiv2023

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

Advances in spatial transcriptomics (ST) technologies have provided unprecedented opportunities to depict transcriptomic and histological landscapes in the spatial context. Multi-modal ST data provide abundant and comprehensive information about cellular status, function, and organization. However, in dealing with the processing and analysis of spatial transcriptomics data, existing algorithms struggle to effectively fuse the multi-modal information contained within ST data. Here, we propose a graph contrastive learning-based cross-modality fusion model named stGCL for accurate and robust integrating gene expression, spatial information as well as histological profiles simultaneously. stGCL adopts a novel histology-based Vision Transformer (H-ViT) method to effectively encode histological features and combines multi-modal graph attention auto-encoder (GATE) with contrastive learning to fuse cross-modality features. In addition, stGCL introduces a pioneering spatial coordinate correcting and registering strategy for tissue slices integration, which can reduce batch effects and identify cross-sectional domains precisely. Compared with state-of-the-art methods on spatial transcriptomics data across platforms and resolutions, stGCL achieves a superior clustering performance and is more robust in unraveling spatial patterns of biological significance. Additionally, stGCL successfully reconstructed three-dimensional (3D) brain tissue structures by integrating vertical and horizontal slices respectively. Application of stGCL in human bronchiolar adenoma (BA) data reveals intratumor spatial heterogeneity and identifies candidate gene biomarkers. In summary, stGCL enables the fusion of various spatial modality data and is a powerful tool for analytical tasks such as spatial domain identification and multi-slice integration.

bioinformatics↗

bioRxiv2023

ADNP Modulates SINE B2-Derived CTCF-Binding Sites during Blastocyst Formation in Mouse

During early embryo development, the nuclear factor CTCF plays a vital role in organizing chromatin structure and regulating transcription. Recent studies have examined the establishment of nucleosome profiles around the CTCF motif sites shortly after fertilization. However, the kinetics of CTCF chromatin occupation in pre-implantation embryos have remained unclear. In this study, we utilized CUT&RUN technology to investigate CTCF occupancy in mouse pre-implantation development. Our findings revealed that CTCF begins binding to the genome prior to zygotic genome activation (ZGA), with a preference for CTCF anchored chromatin loops. Although the majority of CTCF occupancy is consistently maintained, we identified a specific set of binding sites enriched in the mouse-specific short-interspersed element (SINE) family B2, which are restricted to the cleavage stages. Notably, our data suggested that the neuroprotective protein ADNP may counteract the stable association of CTCF at SINE B2-derived CTCF-binding sites.

developmental biology↗

bioRxiv2023

Subregion and sex differences in ethanol activation of cholinergic and glutamatergic cells in the mesopontine tegmentum

Ethanol engages cholinergic signaling and elicits endogenous acetylcholine release. Acetylcholine input to the midbrain originates from the mesopontine tegmentum (MPT), which is composed of the laterodorsal tegmentum (LDT) and the pedunculopontine tegmental nucleus (PPN). We investigated the effect of acute and chronic ethanol administration on cholinergic and glutamatergic neuron activation in the PPN and LDT in male and female mice. We show that ethanol selectively activates neurons of the PPN and not the LDT in male mice. Acute 4.0 g/kg and chronic 15 daily injections of 2.0 g/kg i.p. ethanol induced Fos expression in cholinergic and glutamatergic PPN neurons in male mice, whereas cholinergic and glutamatergic neurons of the LDT were unresponsive. In contrast, acute or chronic ethanol at either dose or duration had no effect on the activation of cholinergic or glutamatergic neurons in the MPT of female mice. Female mice had higher level of baseline activation in cholinergic neurons compared with males. We also found a population of co-labeled cholinergic and glutamatergic neurons in the PPN and LDT which were highly active in the saline- and ethanol-treated groups in both sexes. These findings illustrate the complex differential effects of ethanol across dose, time point, MPT subregion and sex.

pharmacology and toxicology↗