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Ma, Z.

Publications and source records attributed to Ma, Z..

At least 109 records · Page 6Linked to original sources
bioRxiv2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

systems biology↗
bioRxiv2024

All-Optical Multimodal Mapping of Single Cell Type-Specific Metabolic Activities via REDCAT

Metabolism underlies cell growth, survival, and function, yet its activities vary widely across cell types and tissue environments. Spatially resolving these processes in situ at single-cell resolution is essential to advance our understanding of cellular function and tissue physiology in health and disease. However, existing approaches are limited by either destructive workflows, insufficient spatial resolution and biochemical specificity, or lack of direct linkage to cell identity. Here, we present Raman Enhanced Delineation of Cell Atlases in Tissues (REDCAT), a multimodal all-optical platform that integrates stimulated Raman scattering, autofluorescence redox imaging, second harmonic generation, and high-plex immunofluorescence to co-map metabolic activities and cell types within the same tissue section. REDCAT achieves subcellular resolution profiling of protein, lipid, redox, and nuclear acid metabolism, together with extracellular matrix composition, in both FFPE and fresh-frozen human tissues. Applied to normal lymph nodes, REDCAT delineated distinct redox and lipid remodeling programs across germinal center B-cell zones and immune subsets, highlighting cell-type-specific metabolic specialization. In lymphoma, it revealed profound metabolic reprogramming, including extensive lipid accumulation, nuclear metabolic heterogeneity, and a transitional metabolic state associated with transformation from chronic lymphocytic leukemia to diffuse large B-cell lymphoma, thereby illuminating tumor evolution in situ. In human liver, REDCAT resolved cell-type-specific lipid droplet diversity and zonation-dependent nuclear metabolic gradients, uncovering new principles of spatial metabolic organization. By directly linking cell identity with spatial metabolic states at single-cell or subcellular resolution, REDCAT establishes a broadly applicable framework for studying immune function, tumor progression, and tissue physiology, and offers a new path to deciphering the metabolic basis of health and disease.

cell biology↗
bioRxiv2024

Coma pattern-based autofocusing unlocks superior performance in unravelling single-cell dynamics during bacterial cold shock response

Imaging-based single-cell physiological profiling holds great potential for uncovering fundamental bacterial cold shock response (CSR) mechanisms, but its application is impeded by severe focus drift during rapid temperature downshifts required for CSR induction. Here, we introduce LUNA (Locking Under Nanoscale Accuracy), an innovative autofocusing method that leverages the coma pattern of detection light to characterize focus drift. LUNA improves the focusing precision down to 3 nm and extends the focusing range to at least 40 times the objective depth-of-focus. These advancements enable us to investigate the complete dynamics of bacterial single-cell CSR, revealing continuous cellular growth and division. We resolve a three-phase adaptation process characterized by distinct growth deceleration dynamics, and show that bacterial cells maintain robust size regulation and coordinate uniform adaptation to cold shock through synchronized growth and elapsed cycles. Notably, a model based on scattering theory reconciles the paradox between the growth lag of batch culture and continuous single-cell growth. These findings fundamentally transform our understanding of bacterial CSR and highlight LUNAs excellent potential for expanding state-of-the-art research in biology.

microbiology↗
bioRxiv2024

The molecular chronology of mammary epithelial cell fate switching

The adult mammary gland is maintained by lineage-restricted progenitor cells through pregnancy, lactation, involution, and menopause. Injury resolution, transplantation-associated mammary gland reconstitution, and tumorigenesis are unique exceptions, wherein mammary basal cells gain the ability to reprogram to a luminal state. Here, we leverage newly developed cell-identity reporter mouse strains, and time-resolved single-cell epigenetic and transcriptomic analyses to decipher the molecular programs underlying basal-to-luminal fate switching in vivo. We demonstrate that basal cells rapidly reprogram toward plastic cycling intermediates that appear to hijack molecular programs we find in bipotent fetal mammary stem cells and puberty-associatiated cap cells. Loss of basal-cell specifiers early in dedifferentiation coincides with activation of Notch and BMP, among others. Pharmacologic blockade of each pathway disrupts basal-to-luminal transdifferentiation. Our studies provide a comprehensive map and resource for understanding the coordinated molecular changes enabling terminally differentiated epithelial cells to transition between cell lineages and highlights the stunning rapidity by which epigenetic reprogramming can occur in response to disruption of tissue structure.

cell biology↗
bioRxiv2024

Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding

Recent breakthroughs in diffusion-based generative models have prompted de novo protein design, notably in generating diverse and realistic structures. Nevertheless, while existing models either excel at unconditional generation or employ residue-wise conditioning for topological control, explorations on a holistic, top-down approach to control the overall topological arrangements is still limited. In response, we introduce TopoDiff, a diffusion-based framework augmented by a structure encoder and a latent sampler. Our model can unsupervisedly learn a compact latent representation of protein global geometry, while simultaneously integrating a diffusion module to leverage this information for controlled structure generation. In benchmark against existing models, TopoDiff demonstrates comparable performance on established metrics and exhibits an improved coverage over the fold modes of natural proteins. Moreover, our method enables versatile control at the global-geometry level for structural generation, under the assistance of which we derived a number of novel folds of mainly-beta proteins with comprehensive experimental validation.

bioengineering↗
bioRxiv2024

Multi-Modal Large Language Model Enables All-Purpose Prediction of Drug Mechanisms and Properties

Accurately predicting the mechanisms and properties of potential drug molecules is essential for advancing drug discovery. However, traditional methods often require the development of specialized models for each specific prediction task, resulting in inefficiencies in both model training and integration into work-flows. Moreover, these approaches are typically limited to predicting pharmaceutical attributes represented as discrete categories, and struggle with predicting complex attributes that are best described in free-form texts. To address these challenges, we introduce DrugChat, a multi-modal large language model (LLM) designed to provide comprehensive predictions of molecule mechanisms and properties within a unified framework. DrugChat analyzes the structure of an input molecule along with users queries to generate comprehensive, free-form predictions on drug indications, pharmacodynamics, and mechanisms of action. Moreover, DrugChat supports multi-turn dialogues with users, facilitating interactive and in-depth exploration of the same molecule. Our extensive evaluation, including assessments by human experts, demonstrates that DrugChat significantly outperforms GPT-4 and other leading LLMs in generating accurate free-form predictions, and exceeds state-of-the-art specialized prediction models.

pharmacology and toxicology↗
bioRxiv2024

The dynamics of wild Vitis species in response to climate change facilitate the breeding of grapevine and its rootstocks with climate resilience

Climate change poses significant challenges to agricultural suitability and food security due to the limited adaptability of domesticated crops, however, our understanding of their climate adaptability is still limited. In this study, we compare the potential distributional dynamics in response to future climate change between grapevine and its wild relatives (Vitis L.) based on ecological niche models (ENMs). We reveal that the annual mean temperature is the primary factor influencing the potential distribution of cultivated grapes (V. vinifera L.). By 2080, under RCP 8.5, suitable areas for wine and table grapes from V. vinifera are predicted to reduced by 15.04x105 km{superscript 2} and 13.26x105 km{superscript 2}, respectively. The results suggest that grape cultivation, particularly for table grapes, will face serious threats from future climate change. In contrast, seventy percent of wild grapes are well able to withstand future climate conditions. For example, under RCP 8.5, North American wild grapes like V. rotundifolia and V. labrusca, as well as East Asian wild grapes such as V. heyneana and V. davidii, are projected to demonstrate significant adaptability. These wild grapes have the potential to provide rootstocks that could enhance the adaptability of cultivated grapes and serve as invaluable resources for breeding programs aimed at developing new cultivars better suited to withstand the environmental pressures of climate change. Our results not only predict potential suitable distribution areas of wild grapes in the future, but also emphasize the essential impact of these wild genetic resources on grape breeding for promoting adaptation to future climate change.

plant biology↗
bioRxiv2024

Global remodeling of ADP-ribosylation by PARP1 suppresses influenza A virus infection

ADP-ribosylation is a highly dynamic and fully reversible post-translational modification performed by poly(ADP-ribose) polymerases (PARPs) that modulates protein function, abundance, localization and turnover. Here we show that influenza A virus infection causes a rapid and dramatic upregulation of global ADP-ribosylation that inhibits viral replication. Mass spectrometry defined for the first time the global ADP-ribosylome during infection, creating an infection-specific profile with almost 4,300 modification sites on [~]1,080 host proteins, as well as over 100 modification sites on viral proteins. Our data indicate that the global increase likely reflects a change in the form of ADP-ribosylation rather than modification of new targets. Functional assays demonstrated that modification of the viral replication machinery antagonizes its activity and further revealed that the anti-viral activity of PARPs and ADP-ribosylation is counteracted by the influenza A virus protein NS1, assigning a new activity to the primary viral antagonist of innate immunity. We identified PARP1 as the enzyme producing the majority of poly(ADP-ribose) present during infection. Influenza A virus replicated faster in cells lacking PARP1, linking PARP1 and ADP-ribosylation to the anti-viral phenotype. Together, these data establish ADP-ribosylation as an anti-viral innate immune-like response to viral infection antagonized by a previously unknown activity of NS1.

microbiology↗
bioRxiv2024

Bird Name-a-thon: Categorizing English bird names using crowdsourcing

Common names of species are important for communicating with the general public. In principle, these names should provide an accessible way to engage with and identify species. The common names of species have historically been labile without standard guidelines, even within a language. Currently, there is no systematic assessment of how often common names communicate identifiable and biologically relevant characteristics about species. This is a salient issue in ornithology, where common names are used more often than scientific names for species of birds in written and spoken English, even by professional researchers. To gain a better understanding of the types of terminology used in the English-language common names of bird species, a group of 85 professional ornithologists and non-professional contributors classified unique descriptors in the common names of all recognized species of birds. In the AvianLexiconAtlas database produced by this work, each species common name is assigned to one of ten categories associated with aspects of avian biology, ecology, or human culture. Across 10,906 species of birds, 89% have names describing the biology of the species, while the remaining 11% of species have names derived from human cultural references, human names, or local non-English languages. Species with common names based on features of avian biology are more likely to be related to each other or be from the same geographic region. The crowdsourced data collection also revealed that many common names contain specialized or historic terminology unknown to many of the data collectors, and we include these terms in a glossary and gazetteer alongside the dataset. The AvianLexiconAtlas can be used as a quantitative resource to assess the state of terminology in English-language common names of birds. Future research using the database can shed light on historical approaches to nomenclature and how people engage with species through their names.

zoology↗
bioRxiv2024

Functional MRI reveals regional changes of brain activity after five days of focal high-density theta burst stimulation (hdTBS) of the rat brain

BackgroundThe therapeutic effects of transcranial magnetic stimulation (TMS) likely stem from neuroplasticity induced by repeated sessions over time. While animal models offer insights into TMS-induced plasticity, a rodent model that faithfully replicates prolonged TMS conditions in humans is still lacking. Objective/HypothesisDevelop a rat model that mimics the spatial and temporal patterns of TMS in humans. MethodsExperiments were conducted on two cohorts of healthy adult rats (N=33). In cohort 1, rats underwent surgical implantation of microelectrodes for motor evoked potential (MEP) recording. With a rodent-specific coil and the high-density theta burst stimulation (hdTBS) paradigm, under awake condition, rats received daily TMS at 100% motor threshold for five days (days 1-5) to the hindlimb motor cortex. Cortical excitability was measured by input-output (I-O) curves on Day 0 (pre-hdTBS baseline) and Day 6 (post-hdTBS). The second cohort received identical TMS and underwent fMRI to map cerebral blood volume (CBV) on Days 0 and 6. ResultsDaily hdTBS session for 5 days significantly up-shifted I-O curves only in the TMS group (N=9), not in the sham group (N=7), indicating enhanced cortical excitability. fMRI data showed that, compared to sham group (N=9), rats receiving hdTBS (N=8) had increased basal CBV in several brain regions proximal and distal to the stimulation site, suggesting enhanced basal metabolism. Conclusion(s)Daily hdTBS session for 5 days focally delivered to the motor cortex of naive rats significantly altered basal brain activity in a network of brain regions, opening a novel platform for further investigating TMS-induced plasticity.

neuroscience↗
bioRxiv2024

Development of a DNA-encoded library screening method DEL Zipper to empower the study of RNA-targeted chemical matter

To date, RNA-targeted chemical matter is under explored due to a lack of robust screening assays. In this study, we present a novel RNA-targeted small molecule screening approach using a specialized DNA-encoded library (DEL). Our findings reveal that the specialized DEL library, called "DEL Zipper", can significantly reduce single-stranded DNA-RNA region interaction signals during various kinds of RNA selection. By performing the selection against both G-quadruplex, we have identified novel hits that interact with RNA targets and the results are validated through binding. This study demonstrates that the "DEL Zipper" method is a robust screening assay that has potential for discovering small molecule ligands for diverse RNA targets.

molecular biology↗
bioRxiv2024

Instruction-tuned large language models misalign with natural language comprehension in humans

Transformer-based large language models (LLMs) have significantly advanced our understanding of meaning representation in the human brain. However, increasingly large LLMs have been questioned as valid cognitive models due to their extensive training data and their ability to access context thousands of words long. In this study, we investigated whether instruction tuning, another core technique in recent LLMs beyond mere scaling, can enhance models ability to capture linguistic information in the human brain. We evaluated the self-attention of base and fine-tuned LLMs of different sizes against human eye movement and functional magnetic resonance imaging (fMRI) activity patterns during naturalistic reading. We show that scaling has a greater impact than instruction tuning on model-brain alignment, reinforcing the scaling law in brain encoding performance. These finding have significant implications for understanding the cognitive plausibility of LLMs and their role in studying naturalistic language comprehension.

neuroscience↗
bioRxiv2024

TDP-43 mutations-induced defects in miRNA biogenesis and cytotoxicity by differentially obstructing Dicer activity in Drosophila and in vitro

Mutations in the DNA/RNA-binding protein 43 (TDP-43) can cause amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD). As an RNA-binding protein, TDP-43 plays a diverse physiological role in RNA processing and is potentially involved in the pathological progression caused by disease mutations. However, the precise mechanisms linking RNA dysregulation and TDP-43 mutations, which propel disease progression, are not yet fully understood. Here, we demonstrate that TDP-43 and its Drosophila homolog, TBPH, whose mutations crucially perturb Dicer presentation, result in dysregulated miRNA profiles. Genetically modulating the expression or pharmacologically activating Dicer implies unique interactions of TDP-43 A315T and M337V with the Dicer protein, suggesting a specific mechanism of C-terminal disease mutations contributes to the pathological process. Unlike TDP-43 A315T, the M337V mutation causes the assembly of aggregates and reorganizes but functionally preserves the Dicer activity in miRNA processing. Mutations in TDP-43 disrupt miRNA biogenesis by hindering its interaction with Dicer, leading to cytotoxicity and providing mechanistic insight into the pathogenic mutations associated with ALS-FTD.

molecular biology↗
bioRxiv2024

Co-Transplantation-Based Human-Mouse Chimeric Brain Models to Study Human Glial-Glial and Glial-Neuronal Interactions

Human-mouse chimeric brain models, generated by transplanting human induced pluripotent stem cell (hiPSC)-derived neural cells, are valuable for studying the development and function of human neural cells in vivo. Understanding glial-glial and glial-neuronal interactions is essential for unraveling the complexities of brain function and developing treatments for neurological disorders. To explore these interactions between human neural cells in vivo, we co-engrafted hiPSC-derived neural progenitor cells together with primitive macrophage progenitors into the neonatal mouse brain. This approach creates human-mouse chimeric brains containing human microglia, macroglia (astroglia and oligodendroglia), and neurons. Using super-resolution imaging and 3D reconstruction techniques, we examine the dynamics between human neurons and glia, and observe human microglia pruning synapses of human neurons, and often engulfing neurons themselves. Single-cell RNA sequencing analysis of the chimeric brain uncovers a close recapitulation of the human glial progenitor cell population, along with a dynamic stage in astroglial development that mirrors the processes found in the human brain. Furthermore, cell-cell communication analysis highlights significant neuronal-glial and macroglial-microglial interactions, especially the interaction between adhesion molecules neurexins and neuroligins between neurons and astroglia, emphasizing their key role in synaptogenesis. We also observed interactions between microglia and astroglia mediated by SPP1, crucial for promoting microglia growth and astrogliosis, and the PTN-MK pathways, instrumental in homeostatic maintenance and development in macroglial progenitors. This innovative co-transplantation model opens up new avenues for exploring the complex pathophysiological mechanisms underlying human neurological diseases. It holds particular promise for studying disorders where glial-neuronal interactions and non-cell-autonomous effects play crucial roles.

neuroscience↗
bioRxiv2024

Colony pattern development of a synthetic bistable switch

Microbial colony development hinges upon a myriad of factors, including mechanical, biochemical, and environmental niches, which collectively shape spatial patterns governed by intricate gene regulatory networks. The inherent complexity of this phenomenon necessitates innovative approaches to comprehend and compare the mechanisms driving pattern formation. Here, we unveil the multistability of bacterial colony patterns orchestrated by a simple synthetic bistable switch. Utilizing quantitative imaging and spatially resolved transcriptome approaches, we explore the deterministic process of a ring-like colony pattern formation from a single cell. This process is primarily driven by bifurcation events programmed by the gene regulatory network and microenvironmental cues. Additionally, we observe a noise-induced process amplified by the founder effect, leading to patterns of symmetry-break during range expansion. The degrees of asymmetry are profoundly influenced by the initial conditions of single progenitor cells during the nascent stages of colony development. These findings underscore how the process of range expansion enables individual cells, exposed to a uniform growth-promoting environment, to exhibit inherent capabilities in generating emergent, self-organized behaviour.

systems biology↗
bioRxiv2024

Single-cell transcriptomics reveals immunosuppressive microenvironment and highlights stumor-promoting macrophage cells in Glioblastoma

Glioblastoma (GBM) is the most prevalent and aggressive primary brain malignancy in adults. Nevertheless, the cellular heterogeneity and complexity within the GBM microenvironment (TME) are still not fully understood, posing a significant obstacle in the advancement of more efficient immunotherapies for GBM. In this study, we conducted an integrated analysis of 48 tumor fragments from 24 GBM patients at the single-cell level, uncovering substantial molecular diversity within immune infiltrates. We characterized molecular signatures for five distinct tumor-associated macrophage (TAM) subtypes. Notably, the TAM_MRC1 subtype displayed a pronounced M2 polarization signature. Additionally, we identified a subtype of natural killer (NK) cells, designated CD56dim_DNAJB1. This subtype is characterized by an exhausted phenotype, evidenced by an elevated stress signature and enrichment in the PD-L1/PD-1 checkpoint pathway. Our findings also highlight significant cell-cell interactions among malignant glioma cells, TAM, and NK cells within the TME. Overall, this research sheds light on the functional heterogeneity of glioma and immune cells in the TME, providing potential targets for therapeutic intervention in this immunologically cold cancer.

cancer biology↗
bioRxiv2024

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data

Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells molecular profiles with cellular neighborhood and tissue image information.

bioinformatics↗