Biology subjects

Huang, Y.

Publications and source records attributed to Huang, Y..

At least 559 records · Page 31Linked to original sources

bioRxiv2023

Ancient farmer and steppe pastoralist-related founding lineages contributed to the complex landscape of episodes in the diversification of Chinese paternal lineages

Ancient DNA advances have reported the complex genetic history of Eurasians, but how the knowledge of ancient subsistence strategy shifts and population movements influenced the fine-scale paternal genetic structure in East Asia has not been assessed. Here, we reported one integrated Y-chromosome genomic database of 15,530 people, including 1753 ancient people and newly-reported 919 individuals genotyped using our recently-developed targeted sequencing YHSeqY3000 panel, to explore Chinese genomic diversity, population evolutionary tracts and their genetic formation mechanism. We identified four major ancient technological innovations and population movements that shaped the landscape of Chinese paternal lineages. First, the expansion of millet farmers and early East Asians from the Yellow River Basin carrying the major O2/D subclades promoted the formation of the Sino-Tibetan peoples major composition and accelerated the Tibetan Plateaus permanent occupation. Second, rice farmers dispersal from the Yangtze River Valley carrying O1 and some sublineages of O2 contributed significantly to Tai-Kadai, Austronesian, Hmong-Mien, Austroasiatic people and southern Han Chinese. Third, Siberian-related paternal lineages of Q and C originated and boomed from Neolithic hunter-gatherers from the Mongolian Plateau and the Amur River Basin and significantly influenced the gene pools of northern Chinese. Fourth, western Eurasian-derived J, G and R lineages initially spread with Yamnaya steppe pastoralists and other proto-Indo-European people and further widely dispersed via the trans-Eurasian cultural communication along the Eurasian Steppe and the ancient Silk Road, remaining genetic trajectories in northwestern Chinese. Our work provided comprehensive modern and ancient genetic evidence to illuminate the impact of population interaction from the ancient farmer or herder-based societies on the genetic diversity patterns of modern people, revised our understandings of ancestral sources of Chinese paternal lineages, underscored the scientific imperative of the large-scale genomic resources of dense spatiotemporal underrepresented sampling populations to understand human evolutionary history.

genetics↗

bioRxiv2023

Neuronal apoE4 induces early hyperexcitability in select populations of hippocampal neurons by altering Nell2 expression

The full impact of apolipoprotein E4 (APOE4), the strongest genetic risk factor for Alzheimers disease (AD), on neuronal and network function remains unclear. We found hippocampal region-specific network hyperexcitability in young APOE4 knock-in (E4-KI) mice which predicted cognitive deficits at old age. Network hyperexcitability in young E4-KI mice was mediated by hippocampal region-specific subpopulations of smaller and hyperexcitable neurons that were eliminated by selective removal of neuronal APOE4. Aged E4-KI mice exhibited hyperexcitable granule cells, a progressive inhibitory deficit, and E/I imbalance in the dentate gyrus, exacerbating hippocampal hyperexcitability. Single-nucleus RNA-sequencing revealed neuronal cell type-specific and age-dependent transcriptomic changes, including Nell2 overexpression in E4-KI mice. Reducing Nell2 expression in specific neuronal types of E4-KI mice with CRISPRi rescued their abnormal excitability phenotypes, implicating Nell2 overexpression as a cause of APOE4-induced hyperexcitability. These findings highlight the early transcriptomic and electrophysiological alterations underlying APOE4-induced hippocampal network dysfunction and its contribution to AD pathogenesis with aging.

neuroscience↗

bioRxiv2023

Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics

BackgroundThe wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics. MethodsInvestigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments. ResultsWe show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts. ConclusionsThe automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.

biochemistry↗

bioRxiv2023

Plasticity of growth laws tunes resource allocation strategies in bacteria

Bacteria like E. coli grow at vastly different rates on different substrates, however, the precise reason for this variability is poorly understood. Different growth rates have been attributed to nutrient quality, a key parameter in bacterial growth laws. However, it remains unclear to what extent nutrient quality is rooted in fundamental biochemical constraints like the energy content of nutrients, the protein cost required for their uptake and catabolism, or the capacity of the plasma membrane for nutrient transporters. Here, we show that while nutrient quality is indeed reflected in protein investment in substrate-specific transporters and enzymes, this is not a fundamental limitation on growth rate. We show that it is possible to turn mannose, one of the poorest substrates of E. coli, into one of the best substrates by reengineering chromosomal promoters of the mannose transporter and metabolic enzymes required for mannose degradation. However, we show that this faster growth rate comes at the cost of diverse cellular capabilities, reflected in longer lag phases, worse starvation survival and lower motility. We show that addition of cAMP to the medium can rescue these phenotypes but imposes a corresponding growth cost. Based on these data, we propose that nutrient quality is largely a self-determined, plastic property that can be modulated by the fraction of proteomic resources devoted to a specific substrate in the much larger proteome sector of catabolically activated genes. Rather than a fundamental biochemical limitation, nutrient quality reflects resource allocation decisions that are shaped by evolution in specific ecological niches and can be quickly adapted if necessary. Author summaryBacteria grow at very different rates on different substrates. Therefore, the substrates themselves are often denoted as rich substrates versus poor substrates, referring to their nutrient quality. Nutrient quality is also a key parameter in bacterial growth laws that determines substrate-specific growth rate. However, it remains unclear which properties make a specific carbon source a good substrate or a bad substrate and a host of different explanations have been suggested, such as the energy content of the nutrient, the protein investment required for efficient catabolism of the substrate, or limitations in membrane capacity for transporters of the substrate, all of which are actively debated. Here, we show instead that nutrient quality can be a plastic property that can be dialed by evolution or by genetic perturbations. While we report a correlation between nutrient quality and protein cost of substrate utilization, this correlation is not causal. Instead, nutrient quality is encoded by the proteome fraction of a core catabolic sector within a much larger co-regulated catabolic sector. Different nutrients thus enable bacteria to grow, but they also serve as a major signal that allows microbes to infer information about their environment. We propose that nutrient quality encoded in a combination regulatory architecture and enzymatic properties, serves as both as a map of the safety and reliability of the environment and as a regulatory mechanism implementing proteome allocation decisions.

microbiology↗

bioRxiv2023

Long noncoding RNAs and mRNAs profiling in ovary during laying and broodiness in Taihe Black-Bone Silky Fowls (Gallus gallus domesticus Brisson)

Broodiness is one of the important factors affecting poultry egg production, with the black-bone Silky (Gallus gallus domesticus Brisson) exhibiting strong broodiness behavior. Changes in ovarian signaling can offer insights into the mechanisms influencing broodiness. However, research comparing the characteristics of long non- coding RNAs (lncRNAs) in the ovaries during the broody chickens (BC) and high egg- laying chickens (GC) groups of poultry is still limited. In this study, we employed RNA-seq to analyze the ovarian transcriptomes (lncRNAs and mRNAs) of eight brooding and high egg-laying Taihe Black-Bone Silky Fowls (TBsf). We identified a total of 16,444 mRNAs and 18,756 lncRNAs, among which 349 mRNAs and 651 lncRNAs (P < 0.05) showed significantly different expression (DE) between the BC and GC groups. Additionally, we identified the cis-regulated and trans-regulated target genes of differentially abundant lncRNA transcripts and constructed an lncRNA- mRNA trans-regulated interaction network associated with ovarian follicle development. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analyses revealed that DE mRNAs and target genes of DE lncRNAs were associated with neuroactive ligand-receptor interaction, CCR6 chemokine receptor binding, G-protein coupled receptor binding, Cytokine-cytokine receptor interaction, and ECM-receptor interaction pathways. Our study provides a catalogue of lncRNAs and mRNAs related to ovarian development and constructs a predicted interaction network of differentially abundant lncRNAs and Differentially Expressed Genes (DEGs) in TBsf, thus enhancing our understanding of the interactions between lncRNAs and genes regulating brooding behavior. Author summaryIn this study, we performed transcriptome sequencing analysis of the ovaries from BC and GC groups in TBsf. A total of 349 significantly differentially expressed mRNAs and 651 significantly differentially expressed lncRNAs were detected. Furthermore, we identified 10 key genes that may affect reproductive performance in chickens: STC1, MMP13, IL8, AT2, FSHB, RARB, THOC7, FGF12, EPHA1, and NPY5R. Based on GO and KEGG enrichment analyses, we preliminarily explored five pathways that may influence egg production performance: neuroactive ligand-receptor interaction, CCR6 chemokine receptor binding, G-protein coupled receptor binding, cytokine-cytokine receptor interaction and ECM-receptor interaction. These genes and pathways may play crucial roles in improving broodiness behavior. This study establishes a foundation for subsequent screening of candidate functional genes and functional validation of important production traits in TBsf, providing a theoretical basis for further molecular breeding and enhancing egg-laying performance in TBsf.

bioinformatics↗

bioRxiv2023

eQTL mapping in transgenic alpha-synuclein carrying Caenorhabditis elegans recombinant inbred line

Protein aggregation of -synuclein (S) is a genetic and neuropathological hallmark of Parkinsons disease (PD). Studies in the model nematode Caenorhabditis elegans suggested that variation of S aggregation depends on the genetic background. However, which genes and genetic modifiers underlie individual differences in S pathology remains unknown. To study the genotypic-phenotypic relationship of S aggregation, we constructed a Recombinant Inbred Line (RIL) panel derived from a cross between genetically divergent strains C. elegans NL5901 and SCH4856, both harboring the human S gene. As a first step to discover genetic modifiers 70 S-RILs were measured for whole-genome gene expression and expression quantitative locus analysis (eQTL) were mapped. We detected multiple eQTL hot-spots, many of which were located on Chromosome V. To confirm a causal locus, we developed Introgression Lines (ILs) that contain SCH4856 introgressions on Chromosome V in an NL5901 background. We detected 74 genes with an interactive effect between S and the genetic background, including the human p38 MAPK homologue pmk-1 that has previously been associated with PD. Together, we present a unique S-RIL panel for defining effects of natural genetic variation on S pathology, which contributes to finding genetic modifiers of PD.

genomics↗

bioRxiv2023

EZH2 synergizes with BRD4-NUT to drive NUT carcinoma growth through silencing of key tumor suppressor genes

NUT carcinoma (NC) is an aggressive carcinoma driven by the BRD4-NUT fusion oncoprotein, which activates chromatin to promote expression of pro-growth genes. BET bromodomain inhibitors (BETi) impede BRD4-NUTs ability to activate genes and are thus a promising treatment but limited as monotherapy. The role of gene repression in NC is unknown. Here, we demonstrate that EZH2, which silences genes through establishment of repressive chromatin, is a dependency in NC. Inhibition of EZH2 with the clinical compound tazemetostat (taz) potently blocked growth of NC cells. Epigenetic and transcriptomic analysis revealed that taz reversed the EZH2-specific H3K27me3 silencing mark, and restored expression of multiple tumor suppressor genes while having no effect on key oncogenic BRD4- NUT-regulated genes. CDKN2A was identified as the only gene amongst all taz-derepressed genes to confer resistance to taz in a CRISPR-Cas9 screen. Combined EZH2 inhibition and BET inhibition synergized to downregulate cell proliferation genes resulting in more pronounced growth arrest and differentiation than either inhibitor alone. In pre-clinical models, combined taz and BETi synergistically blocked growth and prolonged survival of NC-xenografted mice, with all mice cured in one cohort. STATEMENT OF SIGNIFICANCEIdentification of EZH2 as a dependency in NC substantiates the reliance of NC tumor cells on epigenetic dysregulation of functionally opposite, yet highly complementary chromatin regulatory pathways to maintain NC growth. In particular, repression of CDKN2A expression by EZH2 provides a mechanistic rationale for combining EZH2i with BETi for the clinical treatment of NC.

cancer biology↗

bioRxiv2023

Large-scale population genomics of Malayan pangolins reveals deep diversification and a new species

BackgroundArchipelagos and oceanic islands often present high percentage of endemism due to rapid speciation. The Malayan pangolin is a species distributing at both mainland (southern Yunnan, China) and oceanic islands via Malayan peninsula, which may result in deep differentiation among populations. In-depth investigation of population structure and genetic consequences for such species is of vital importance for their protection and conservation, practically for the critically endangered Malayan pangolin that is suffering from poaching, illegal trade, and habitat loss. ResultsHere we carried out a large-scale population genomic analysis for Malayan pangolins, and revealed three highly distinct genetic populations in this species, two of which are now being reported for the first time. Based on multiple lines of genomic and morphological evidence, we postulate the existence of a new pangolin species (Manis_1). Genetic diversity and recent inbreeding were both at a moderate level for both Malayan pangolins and Manis_1, but mainland Malayan pangolins presented relatively lower genetic diversity, higher inbreeding and fitness cost than island populations. ConclusionsWe found extremely deep and graded differentiation in Malayan pangolins, with two newly discovered genetic populations and a new pangolin species that is closely related to the Philippine pangolin than the typical Malayan pangolin, but a distant relative of the Indian pangolin. Anthropogenic factors did not significantly weaken the basis of genetic sustainability for Malayan pangolins, but mainland Malayan pangolins should be paid more attention for conservation due to higher genetic risks than island populations.

genomics↗

bioRxiv2023

Multi-color live-cell optical nanoscopy using phasor analysis

Stimulated emission depletion microscopy (STED) is a powerful tool for studying nanoscale cell structure and activity, but the difficulties it encounters in multicolor imaging limit its application in biological research. To overcome the disadvantages of limited number of channels and high cost of multicolor STED imaging based on spectral identity, we introduced lifetime into live-cell multicolor STED imaging by separating selected dyes of the same spectrum by phasor analysis. Experimental results show that our method enables live-cell STED imaging with at least 4 colors, enabling observation of cellular activity beyond the diffraction limit.

cell biology↗

bioRxiv2023

Generative Neural Spike Prediction from Upstream Neural Activity via Behavioral Reinforcement

It is quite challenging to predict dynamic stimulation patterns on downstream cortical regions from upstream neural activities. Spike prediction models used in traditional methods are trained by downstream neural activity as the reference signal in a supervised manner. However, downstream activity is unavailable when neurological disorders exist. This study proposes a reinforcement learning-based point process framework to generatively predict spike trains through behavior-level rewards, solving the difficulty. The framework is evaluated to reconstruct the transregional spike communication during motor control through behavioral reinforcement. We show that our methods can generate spike trains beyond the collected neural recordings and achieve better behavioral performance.

bioengineering↗

bioRxiv2023

Microglia Regulate Sleep via Calcium-Dependent Modulation of Norepinephrine Transmission

Sleep interacts reciprocally with immune system activity, but its specific relationship with microglia - the resident immune cells in the brain - remains poorly understood. Here we show that microglia can regulate sleep through a mechanism involving Gi-coupled GPCRs, intracellular Ca2+ signaling, and suppression of norepinephrine transmission. Chemogenetic activation of microglia Gi signaling strongly promoted sleep, whereas pharmacological blockade of Gi-coupled P2Y12 receptors decreased sleep. Two-photon imaging showed that P2Y12/Gi activation elevated microglia intracellular Ca2+, and blockade of this Ca2+ elevation largely abolished the Gi-induced sleep increase. Microglia Ca2+ level also increased at natural wake-to-sleep transitions, caused partly by reduced norepinephrine. Furthermore, imaging of norepinephrine activity with its biosensor showed that microglia P2Y12/Gi activation significantly reduced norepinephrine, partly by increasing the adenosine concentration. Thus, microglia can regulate sleep through reciprocal interactions with norepinephrine transmission.

neuroscience↗

bioRxiv2023

APOE4/4 is linked to damaging lipid droplets in Alzheimer's microglia

Several genetic risk factors for Alzheimers Disease (AD) implicate genes involved in lipid metabolism and many of these lipid genes are highly expressed in glial cells. However, the relationship between lipid metabolism in glia and AD pathology remains poorly understood. Through single-nucleus RNA-sequencing of AD brain tissue, we have identified a microglial state defined by the expression of the lipid droplet (LD) associated enzyme ACSL1 with ACSL1-positive microglia most abundant in AD patients with the APOE4/4 genotype. In human iPSC-derived microglia (iMG) fibrillar A{beta} (fA{beta}) induces ACSL1 expression, triglyceride synthesis, and LD accumulation in an APOE-dependent manner. Additionally, conditioned media from LD-containing microglia leads to Tau phosphorylation and neurotoxicity in an APOE-dependent manner. Our findings suggest a link between genetic risk factors for AD with microglial LD accumulation and neurotoxic microglial-derived factors, potentially providing novel therapeutic strategies for AD.

neuroscience↗

bioRxiv2023

Contextualizing protein representations using deep learning on protein networks and single-cell data

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce PO_SCPLOWINNACLEC_SCPLOW, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, PO_SCPLOWINNACLEC_SCPLOW learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PO_SCPLOWINNACLEC_SCPLOWs embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs effects across cell types. PO_SCPLOWINNACLEC_SCPLOW outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. PO_SCPLOWINNACLEC_SCPLOWs ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

bioinformatics↗

bioRxiv2023

Using UAV-based temporal spectral indices to dissect changes in the stay green trait in wheat

Stay green (SG) in wheat, a beneficial trait for increasing yield and stress resistance, needs to be supported by analysis of the underlying genetic basis. Spectral reflectance indices (SIs) provide non-destructive tools to evaluate crop temporal senescence. However, few SI-based SG quantification pipelines for analyzing diverse wheat panels in the field are available. Here, we first applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages during two field seasons. Based on over 12,000 SIs data set, four SIs (NDVI, GNDVI, NDRE and OSAVI) were selected to develop relative stay green scores (RSGS) and the senescence of wheat populations occurs mainly at four developmental stages stage 1 (S1) to S4, accounting for the final SG indicators. A RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 SNPs significantly associated with RSGS and 1,085 corresponding candidate genes in the two seasons; 15 QTL overlapped or were adjacent to known SG-related QTL or genes and the remaining QTL were novel. Finally, we selected three superior candidate genes (TraesCS6B03G0356400, TraesCS2B03G1299500, and TraesCS2A03G1081100) as examples by transcriptomes, gene annotation, and gene-based association analysis for further analysis and found that utilization of superior SG-related variation in China gradually increased following the Green Revolution. The study provides a useful reference for further SG-related gene discovery of favorable variations in diverse wheat panels.

genetics↗

bioRxiv2023

BNIP3-mediated mitophagy boosts the competitive dominant growth of lenvatinib resistant cells via reprogramming energy metabolism in HCC

Although increasing studies has demonstrated that cell competition widely involved in the growth and homeostasis of multicellular organisms is closely linked to tumorigenesis and development, the mechanistic contributions to the association between tumor cell competition-driven heterogeneity and drug resistance remains ill-defined. In our study, lenvitinib-resistant hepatocellular carcinoma (HCC) cells display obviously competitive growth dominance against sensitive cells through reprogramming energy metabolism. Mechanistically, when BCL2 interacting protein3 (BNIP3) overexpression activates mitophagy activity in lenvatinib-resistant HCC cells, energy imbalance signal caused by reduced mitochondrial oxidative phosphorylation levels provokes the phosphorylation of AMP-activated protein kinase (AMPK) sensor; subsequently, enabled AMPK specifically targets enolase 2 (ENO2) to enhance glycolysis and eventually promots the competitive capacity and dominant growth. Of note, BNIP3 deficiency shows certain inhibition of cell competition outcome. Our findings emphasize a vital role for BNIP3-AMPK-ENO2 signaling in maintaining the competitive outcome of lenvitinib-resistant HCC cells via regulating energy metabolism; meanwhile this work recognaizes BNIP3 as a promising target to overcome HCC drug resistance.

cell biology↗

bioRxiv2023

Learning Complete Protein Representation by Deep Coupling of Sequence and Structure

Learning effective representations is crucial for understanding proteins and their biological functions. Recent advancements in language models and graph neural networks have enabled protein models to leverage primary or tertiary structure information to learn representations. However, the lack of practical methods to deeply co-model the relationships between protein sequences and structures has led to suboptimal embeddings. In this work, we propose CoupleNet, a network that couples protein sequence and structure to obtain informative protein representations. CoupleNet incorporates multiple levels of features in proteins, including the residue identities and positions for sequences, as well as geometric representations for tertiary structures. We construct two types of graphs to model the extracted sequential features and structural geometries, achieving completeness on these graphs, respectively, and perform convolution on nodes and edges simultaneously to obtain superior embeddings. Experimental results on a range of tasks, such as protein fold classification and function prediction, demonstrate that our proposed model outperforms the state-of-the-art methods by large margins.

bioinformatics↗

bioRxiv2023

Molecular physiological characterization of the dynamics of persister formation in Staphylococcus aureus.

Bacteria possess the ability to enter a growth arrested state known as persistence in order to survive antibiotic exposure. Clinically, persisters are regarded as the main causative agents for chronic and recurrent infectious diseases. To combat this antibiotic-tolerant population, a better understanding of the molecular physiology of persisters is required. In this study, we collected samples at different stages of the biphasic kill curve to reveal the dynamics of the cellular molecular changes that occur in the process of persister formation. After exposure to antibiotics with different modes of action, namely vancomycin and enrofloxacin, similar persister levels were obtained. Both shared and distinct stress responses were enriched for the respective persister populations. However, the dynamics of the presence of proteins linked to the persister phenotype throughout the biphasic kill curve and the molecular profiles in a stable persistent population did show large differences depending on the antibiotic used. This suggests that persisters at the molecular level are highly stress specific, emphasizing the importance of characterizing persisters generated under different stress conditions. Additionally, although generated persisters exhibited cross-tolerance toward tested antibiotics, combined therapies were demonstrated to be a promising approach to reduce persister levels. In conclusion, this investigation sheds light on the stress-specific nature of persisters, highlighting the necessity of tailored treatment approaches and the potential of combined therapy. ImportanceBy monitoring proteome and metabolites during Staphylococcus aureus persister formation under vancomycin and enrofloxacin exposure, we revealed the dynamic information of the molecular physiology of persister formation upon exposure to two different antibiotics with different modes of action. The data shows that cells that phenotypically are similarly classified as persisters, do have several molecular characteristics in common but, remarkably so, differ substantially in a significant number of other aspects of their molecular makeup. These contrasts provided valuable insights into persister eradication, which holds considerable clinical relevance.

microbiology↗

bioRxiv2023

Pathway Centric Analysis for single-cell RNA-seq and Spatial Transcriptomics Data with GSDensity

Advances in single-cell technology have enabled molecular cellular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Although cluster-centric approaches followed by gene-set analysis can reveal distinct cell types and states, they have limited power in dissecting and interpretating highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. We show that GSDensity can not only accurately detect biologically distinct gene sets but also reveal novel cell-pathway associations that are ignored by existing methods. This is particularly evident in characterizing cancer cell states that are transcriptomically distinct but are driven by shared tumor-immune interaction mechanisms. Moreover, we show that GSDensity, combined with trajectory analysis can identify pathways that are active at various stages of mouse brain development. Finally, we show that GSDensity can identify spatially relevant pathways in mouse brains including those following a high-order organizational patterns in the ST data. We also created a pan-cancer pathway activity ST map, which revealed pathways spatially relevant and recurrently active across six different tumor types. GSDensity is available as an open-source R package and can be widely applied to single-cell and ST data generated by various technologies.

bioinformatics↗