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Biology subjects

Xue, A.

Publications and source records attributed to Xue, A..

At least 19 recordsLinked to original sources

Subcellular transcriptome sequencing with single cell APEX-seq identifies regulators of cell-cell interactions

Single-cell RNA sequencing has transformed our understanding of tissue complexity and heterogeneous cell states, yet provides little information about the subcellular organization of transcriptomes - despite the central role of RNA localization in splicing, translation, and function. Here we introduce single-cell APEX-seq (scAPEX-seq), a proximity labeling-based method for mapping subcellular transcriptomes at single-cell resolution. Improvements in probe design and RNA recovery enable APEX integration with droplet-based RNA-seq to capture endoplasmic reticulum-associated transcripts from thousands of individual cells. Applied to tumor-macrophage co-cultures, ER-targeted scAPEX-seq revealed interaction-dependent cell states and transcriptomic signatures by enriching for cell surface and secretory transcripts that are poorly resolved by conventional scRNA-seq. We further applied scAPEX-seq to short- and long-term co-cultures of HER2+ tumor cells with human chimeric antigen receptor (CAR) T cells, resolving distinct activated CAR T cell states, including populations characterized by upregulated NT5E or CTSW expression. We showed that overexpression of CTSW, a cathepsin protease, in CAR T cells promotes stem-like phenotypes, long-term proliferation, and sustained tumor cell killing. scAPEX-seq provides a powerful and scalable approach for profiling subcellular RNA populations, enabling the discovery of cell-cell interaction regulators missed by conventional approaches.

cell biology↗

Impact of Censoring on the Quality of Cortical Parcellations and Personalized TMS Targets

Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates. Here, we test the efficacy of various censoring strategies on individual-specific cortical parcellations and personalized transcranial magnetic stimulation (TMS) target selection. Using precision-fMRI datasets comprising 50 individuals, we define individualized "ground-truth" references from [≥]1 hour of low-motion data per participant. We then simulate 10-min or 20-min rs-fMRI sessions with varying motion levels from the remaining data, yielding final samples of 22 and 19 participants, respectively. Higher motion produces parcellations and TMS targets that deviate further from the ground-truth references. However, at any given motion level, lenient censoring produces higher quality parcellations and personalized TMS targets than strict censoring. The improvement is comparable to doubling scan duration from 20 to 40 min under strict censoring. With personalized connectome-guided TMS, a common dilemma is whether to re-scan patients with only high-motion runs. A mixed-motion session with one low-motion run and one high-motion run may often be considered usable after discarding the high-motion run and strict censoring. We find that lenient censoring of high-motion-only sessions yields TMS targets comparable to - or even better than - those derived from strictly censored mixed-motion sessions. Therefore, within the motion range and parcellation/TMS targeting frameworks evaluated here, patients may not need to be re-scanned solely because all runs exceed strict censoring criteria.

neuroscience↗

keju: powerful and accurate inference in Massively Parallel Reporter Assays

Massively Parallel Reporter Assays (MPRAs) interrogate the regulatory function of thousands of designed genetic elements in parallel through linked DNA and RNA readouts using an engineered construct and attached minimal reporter. Given the complexity of MPRA experimental designs, several different sources of uncertainty complicate inference. We show that previous methods do not account for substantial differences in uncertainty levels between the DNA and RNA counts and between batches. Accordingly, we present keju, a hierarchical statistical model that estimates candidate transcription rate, differential activity between conditions, and effects from promoter composition for MPRA data. To maximize statistical power and improve false positive rate control, keju conditions on the DNA counts to model batch-specific and modality-specific uncertainty in the RNA counts. keju shows vastly improved sensitivity (59%) in simulations compared to previous methods (31% for MPRAnalyze and 9% for BCalm), and also has lower, more robust false positive rates, calling only 6.8% of unlabeled negative controls significant in real data (compared to 34% for MPRAnalyze and 12% for BCalm).

bioinformatics↗

Therapeutic interleukin-2 rewires skeletal-immune circuits to reverse postmenopausal bone loss

Postmenopausal osteoporosis results from excessive osteoclast activity, but the immune circuits that limit osteoclastogenesis in the bone marrow remain poorly understood. Here, we tested a clinically accessible immunotherapy low-dose interleukin-2 (IL-2) as a therapeutic intervention in mice with established ovariectomy-induced bone loss, integrating microcomputed tomography imaging, single-cell transcriptomics, genetic perturbation, and human monocyte-osteoclast assays. Therapeutic administration of IL-2 reversed trabecular bone loss, expanded CXCR3 regulatory T cells in bone marrow, and promoted IL-10- and CGRP-producing ILC2s that were required for protection. IL-2 also acted directly on the osteoclast lineage, suppressing mouse and human osteoclastogenesis through activation of a STAT1-IRF1-dependent IFN program in RANKL-stimulated monocytes. Together, these data define an IL-2-responsive skeletal-immune network that can be therapeutically engaged to suppress osteoclastogenesis, which opens new avenues for cytokine-based immunotherapies in postmenopausal osteoporosis and related bone disorders.

immunology↗

Multiplexed Perturbation Enables Scalable Pooled Screens

CRISPR-based genetic perturbation screens have revolutionized the ability to link genes to cellular phenotypes with unprecedented precision and scale. However, conventional pooled CRISPR screens require large cell numbers to achieve adequate sgRNA representation, posing technical and financial challenges. Here, we investigate the impact of co-delivery of multiple guide RNAs via high multiplicity of infection (MOI) in pooled CRISPR interference (CRISPRi) screens as a strategy to enhance screening efficiency while reducing cell numbers. We systematically evaluate screen performance across varying MOIs, assessing the effects of multiplexing on knockdown efficiency, sgRNA representation, and potential interference of multiple sgRNA phenotypes. Our data demonstrate that sgRNA multiplexing (MOI 2.5-10) can maintain screen performance while enabling significant reductions in cell number requirements. We further apply these optimized conditions to conduct a genome-wide CRISPR screen for regulators of the intracellular adhesion molecule ICAM-1, successfully identifying novel candidates using as few as half a million cells. This study provides a framework for adopting multiplexed sgRNA strategies to streamline CRISPR screening applications in resource-limited settings.

molecular biology↗

Identifying the Minimal Number of Protein Markers for Cell Type Annotation Using MiniMarS

Over the past decade, there has been an explosion in the characterisation and discovery of cell populations using single-cell technologies. Single-cell multi-omics data, particularly those incorporating gene and protein expression, are increasingly commonplace and can lead to more refined characterisation of cell types. A common challenge for biologists is to isolate cells of interest using a minimal number of markers for cytometry experiments. Although several methods exist for marker selection, there is limited guidance on the relative performance of these methods, and a wrapper package that combines multiple methods is lacking. The method that performs best can vary depending on the dataset and it can be challenging for researchers to test multiple methods for a given dataset. To address these issues, we present MiniMarS (Minimal Marker Selection), an R package that serves as a wrapper for 10 different algorithms. It allows users to determine the best-performing algorithm for identifying the optimal number of markers that will delineate cell populations in their dataset. MiniMarS uses pre-annotated cells with protein features from CyTOF or sequencing-based assays such as CITE-seq and Abseq as input. Outputs include 1) the minimum number of protein markers required to identify the annotated cell populations using a range of marker selection algorithms, and 2) a range of metrics to evaluate the performance of each algorithm. MiniMarS effectively differentiated populations across various datasets, including those from human blood, bone marrow, thymus, mouse spleen, and lymph nodes, even after subsampling over 41,000 cells to 2,500 cells. MiniMarS also identified 15 markers from CITE-seq data, which were then used to successfully identify the same 11 cell subsets in a CyTOF dataset (F1 score>0.9). Additionally, we showed that by appropriately combining clusters, MiniMarS improves the F1 score of a rare population identification (<1% of total cells) by 28.7%. Together, these findings highlight the broad applicability of MiniMarS in identifying appropriate markers for distinguishing cell populations.

bioinformatics↗

Network-based Near-Scalp Personalized Brain Stimulation Targets

Functional connectivity (FC) is often used to identify personalized targets for transcranial magnetic stimulation (TMS). However, existing methods often overlook individual differences in whole-cortex network organization. Furthermore, in some personalized TMS protocols, lower stimulation intensity is used for targets closer to the scalp, which may improve patient tolerance. Here, we develop an algorithm to simultaneously optimize FC and scalp proximity for target localization. We first use the multi-session hierarchical Bayesian model (MS-HBM) to estimate high-quality individual-specific cortical networks. A tree-based algorithm is then used to select the optimal target. With essentially no parameter to tune, our framework may potentially improve generalizability across populations. We compare our approach to existing "cluster" and "cone" algorithms. In two test-retest datasets of healthy individuals from the United States and Singapore, tree-based MS-HBM reliably identifies personalized TMS targets for depression near the scalp. Tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and FC to the subgenual anterior cingulate cortex (sACC) in new out-of-sample MRI sessions. To demonstrate versatility, the same algorithm identifies personalized anxiety targets without tuning any parameter. In patients with treatment-resistant depression, tree-based MS-HBM targets compare favorably with cluster and cone targets in terms of reliability, scalp proximity, and sACC FC, hypothetically reducing stimulation intensity by 15% and 5% respectively. MS-HBM also exhibits the best (most negative) electric-field hotspot sACC FC and highest reliability in induced electric fields. Overall, tree-based MS-HBM provides a robust, generalizable framework to estimate near-scalp personalized targets across populations.

neuroscience↗

Computational design of conformation-biasing mutations to alter protein functions

Most natural proteins alternate between distinct conformational states, each associated with specific functions. Intentional manipulation of conformational equilibria could lead to improved or altered protein properties. Here we develop Conformational Biasing (CB), a rapid and streamlined computational method that utilizes contrastive scoring by inverse folding models to predict variants biased towards desired conformational states. We validated CB across seven diverse deep mutational scanning datasets, successfully predicting variants of K-Ras, SARS-CoV-2 spike, {beta}2 adrenergic receptor, and Src kinase with improved conformation-specific functions including enhanced effector binding or enzymatic activity. Furthermore, applying CB to lipoic acid ligase, a conformation-switching bacterial enzyme that has been used for the development of protein labeling technologies, revealed a previously unknown mechanism for conformational gating of sequence-specificity. Variants biased toward the "open" conformation were highly promiscuous, while "closed" conformation-biased variants were even more specific than wild-type, enhancing the utility of LplA for site-specific protein labeling with fluorophores in living cells. The speed, simplicity, and versatility of CB (available at: https://github.com/alicetinglab/ConformationalBiasing/) suggest that it may be broadly applicable for understanding and engineering protein conformational dynamics, with implications for basic research, biotechnology, and medicine.

biophysics↗

Structure of the microtubule anchoring factor NEDD1 bound to the γ-tubulin ring complex

The {gamma}-tubulin ring complex ({gamma}-TuRC) is an essential multiprotein assembly, in which {gamma}-tubulin, GCP2-6, actin, MZT1 and MZT2 form an asymmetric cone-shaped structure that provides a template for microtubule nucleation. The {gamma}-TuRC is recruited to microtubule organizing centers (MTOCs), such as centrosomes and pre-existing mitotic spindle microtubules, via the evolutionarily-conserved attachment factor NEDD1. NEDD1 contains an N-terminal WD40 domain that binds to microtubules, and a C-terminal domain that associates with the {gamma}-TuRC. However, the structural basis of the NEDD1-{gamma}-TuRC interaction is not known. Here, we report cryo-electron microscopy (cryo-EM) structures of NEDD1 bound to the human {gamma}-TuRC in the absence or presence of the activating factor CDK5RAP2, which interacts with GCP2 to induce conformational changes in the {gamma}-TuRC and promote its microtubule nucleating function. We found that the C-terminus of NEDD1 forms a tetrameric -helical assembly that contacts the lumen of the {gamma}-TuRC cone, is anchored to GCP4, 5 and 6 via protein modules consisting of MZT1 & GCP3 subcomplexes, and orients its microtubule-binding WD40 domains away from the complex. We biochemically tested our structural models by identifying NEDD1 mutants unable to pull-down{gamma} -tubulin from cultured cells. The structure of the {gamma}-TuRC simultaneously bound to NEDD1 and CDK5RAP2 reveals that both factors can associate with the "open" conformation of the complex. Our results show that NEDD1 does not induce conformational changes in the {gamma}-TuRC, but suggest that anchoring of {gamma}-TuRC-capped microtubules by NEDD1 would be structurally compatible with the significant conformational changes experienced by the {gamma}-TuRC during microtubule nucleation.

biochemistry↗

Polymorphic tandem repeats shape single-cell gene expression across the immune landscape

Tandem repeats (TRs) - highly polymorphic, repetitive sequences across the human genome - are important regulators of gene expression but remain underexplored due to challenges in accurate genotyping and analysis1. Here, we generate new whole genome and single-cell RNA sequencing from >5.4 million blood-derived cells across 1,925 individuals in two cohorts [Cuomo et al., accompanying manuscript], and perform meta-analysis to characterize the impact of variation in >1.7 million TR loci on immune cell type-specific gene expression. We identify >69,000 single-cell expression TR loci (sc-eTRs), 30.7% of which are specific to one of 28 immune cell types, and reveal dynamic regulatory effects using cell-state inference. Matched single-cell ATAC sequencing profiles from >3.4 million nuclei in 922 individuals [Xue et al., accompanying manuscript]. uncover chromatin accessibility QTLs for nearly one-third of expression-associated TRs, supporting coordinated effects on cis-regulatory architecture. Fine-mapping implicates 1,490 TRs as candidate causal drivers of gene expression in 6.1% of tested genes, and colocalization analyses highlight >200 genes in which TRs likely mediate genetic associations with immune and hematological traits. Together, these results provide a genome-wide, multiomic view of TR-mediated regulation in the human immune system, establishing TRs as key contributors to cell type-specific regulatory variation and complex trait architecture.

genomics↗

Genetic variants associated with cell-type-specific intra-individual gene expression variability reveal new mechanisms of genome regulation

Gene expression levels can vary substantially across cells, even in a seemingly homogeneous cell population. Identifying the relationships between genetic variation and gene expression is critical for understanding the mechanisms of genome regulation. However, the genetic control of gene expression variability among the cells within individuals has yet to be extensively examined. This is primarily due to the statistical challenges, such as the need for sufficiently powered cohorts and adjusting mean-variance dependence. Here, we introduce MEOTIVE (Mapping genetic Effects On inTra-Individual Variability of gene Expression), a novel statistical framework to identify genetic effects on the gene expression variability (sc-veQTL) accounting for the mean-variance dependence. Using single-cell RNA-seq data of 1.2 million peripheral blood mononuclear cells from 980 human donors, we identified 14 - 3,488 genes with significant sc-veQTLs (study-wide q-value < 0.05) across different blood cell types, 2,103 of which were shared across more than one cell type. We further detected 55 SNP-gene pairs (in 34 unique genes) by directly linking genetic variations with gene expression dispersion (sc-deQTL) regardless of mean-variance dependence, and these genes were enriched in biological processes relevant to immune response and viral infection. An example is rs1131017 (p<9.08x10-52), a sc-veQTL in the 5UTR of RPS26, which shows a ubiquitous dispersion effect across cell types, with higher dispersion levels associated with lower auto-immune disease risk, including rheumatoid arthritis and type 1 diabetes. Another example is LYZ, which is associated with antibacterial activity against bacterial species and was only detected with a monocyte-specific deQTL (rs1384) located at the 3 UTR region (p=1.48x10-11) and replicated in an independent cohort. Our results demonstrate an efficient and robust statistical method to identify genetic effects on gene expression variability and how these associations and their involved pathways confer auto-immune disease risk. This analytical framework provides a new approach to unravelling the genetic regulation of gene expression at the single-cell resolution, advancing our understanding of complex biological processes.

genomics↗

Structure of the γ-tubulin ring complex-capped microtubule

Microtubules are composed of /{beta}-tubulin dimers positioned head-to-tail to form protofilaments that associate laterally in varying numbers. It is not known how cellular microtubules assemble with the canonical 13-protofilament architecture, resulting in micrometer-scale /{beta}-tubulin tracks for intracellular transport that align with, rather than spiral along, the filaments long-axis. We report that the human [~]2.3MDa {gamma}-tubulin ring complex ({gamma}-TuRC), an essential regulator of microtubule formation that contains 14 {gamma}-tubulins, selectively nucleates 13-protofilament microtubules. Cryo-EM reconstructions of {gamma}-TuRC-capped microtubule minus-ends reveal the extensive intra- and inter-domain motions of {gamma}-TuRC subunits that accommodate its actin-containing luminal bridge and establish lateral and longitudinal interactions between {gamma}- and -tubulins. Our structures reveal how free {gamma}-TuRC, an inefficient nucleation template due to its splayed conformation, transforms into a stable cap that blocks addition or loss of /{beta}-tubulins from minus-ends and sets the lattice architecture of cellular microtubules. One Sentence SummaryStructural insights into how the {gamma}-tubulin ring complex nucleates and caps a 13-protofilament microtubule.

biochemistry↗

A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across {approx} 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value [Formula]). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.

genetics↗

Within-Individual Organization of the Human Cerebral Cortex: Networks, Global Topography, and Function

The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian sModel (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.

neuroscience↗

Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity

Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local- global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (GITHUB_LINK).

neuroscience↗

Pitfalls and opportunities for applying PEER factors in single-cell eQTL analyses

Using latent variables in gene expression data can help correct spurious correlations due to unobserved confounders and increase statistical power for expression Quantitative Trait Loci (eQTL) detection. Probabilistic Estimation of Expression Residuals (PEER) is a widely used statistical method that has been developed to remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, its performance has not been largely evaluated in single-cell eQTL data analysis, where it is becoming a commonly used technique. Potential challenges arise due to the structure of single-cell data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that this method requires additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid PEER factors. By using a population-scale single-cell cohort (OneK1K, N = 982), we found that generating PEER factors without further QC or transformation on the pseudo-bulk matrix could result in inferred factors that are highly correlated (Pearsons correlation r = 0.626[~]0.997). Similar spurious correlations were also found in PEER factors inferred from an independent dataset (induced pluripotent stem cells, N = 31). Optimization of the strategy for generating PEER factors and incorporating the improved PEER factors in the eQTL association model can identify 9.0[~]23.1% more eQTLs or 1.7%[~]13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and PEER factors fitted varied significantly for different cell types. In addition, using highly variable genes (e.g., top 2000) to generate PEER factors could achieve similar eGenes discovery power as using all genes but save considerable computational resources ([~]6.2-fold faster). We provide diagnostic guidelines to improve the robustness and avoid potential pitfalls when generating PEER factors for single-cell eQTL association analyses.

bioinformatics↗

A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues

Although cellular senescence is increasingly recognized as driving multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), in vivo senescent cell identification, particularly in bulk or single cell RNA-sequencing (scRNA-seq) data remains challenging. Here, we generated a novel gene set (SenMayo) and first validated its enrichment in bone biopsies from two aged human cohorts. SenMayo also identified senescent cells in aged murine brain tissue, demonstrating applicability across tissues and species. For direct validation, we demonstrated significant reductions in SenMayo in bone following genetic clearance of senescent cells in mice, with similar findings in adipose tissue from humans in a pilot study of pharmacological senescent cell clearance. In direct comparisons, SenMayo outperformed all six existing senescence/SASP gene sets in identifying senescent cells across tissues and in demonstrating responses to senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from publicly available human and murine bone marrow/bone scRNA-seq data and identified monocytic and osteolineage cells, respectively, as showing the highest levels of senescence/SASP genes. Using pseudotime and cellular communication patterns, we found senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Moreover, using this senescence panel, we were able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways associated with these cells, which may be particularly useful for evolving efforts to map senescent cells (e.g., SenNet). In addition, SenMayo represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

cell biology↗

The putative causal effect of type 2 diabetes in risk of cataract: a Mendelian randomization study in East Asian

BackgroundThe epidemiological association between type 2 diabetes and cataract has been well-established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a causal relationship. MethodsWe utilized East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase=36,614, Ncontrol=155,150) and cataract (Ncase=24,622, Ncontrol=187,831) to comprehensively investigate the shared genetics between the two diseases. We performed 1. linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics ({rho}-HESS) to estimate the genetic correlation and local genetic correlation between type 2 diabetes and cataract; 2. multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and 3. Summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the causality. ResultsWe observed a strong genetic correlation (rg=0.58; p-value=5.60x10-6) between type 2 diabetes and cataract. Both {rho}-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% confidence interval [CI] ranging from 1.06 to 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG ({beta}SMR=-0.34, p-value=6.41x10-8) and KCNK17 ({beta}SMR=-0.07, p-value=2.49x10-10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract. ConclusionsOur results provided robust evidence supporting a causal effect of type 2 diabetes on the risk of cataract in East Asians, and posed new paths on guiding prevention and early-stage diagnosis of cataract in type 2 diabetes patients. Key MessagesO_LIWe utilized genome-wide association studies of type 2 diabetes and cataract in a large Japanese population-based cohort and find a strong genetic overlap underlying the two diseases. C_LIO_LIWe performed multiple Mendelian randomization models and consistently disclosed a putative causal effect of type 2 diabetes on the development of cataract. C_LIO_LIWe revealed two candidate genes MIR4453HG and KCNK17 whose expression levelss are likely relevant to the causality between type 2 diabetes and cataract. C_LIO_LIOur study provided theoretical fundament at the genetic level for improving early diagnosis, prevention and treatment of cataract in type 2 diabetes patients in clinical practice C_LI

genetics↗