Biology subjects

Ye, C. J.

Publications and source records attributed to Ye, C. J..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

ADPKD-Causing Missense Variants in Polycystin-1 Disrupt Cell Surface Localization or Polycystin Channel Function

Autosomal dominant polycystic kidney disease (ADPKD) is the leading monogenic cause of kidney failure and affects millions of people worldwide. Despite the prevalence of this monogenic disorder, our limited mechanistic understanding of ADPKD has hindered therapeutic development. Here, we successfully developed bioassays that functionally classify missense variants in polycystin-1 (PC1). Strikingly, ADPKD pathogenic missense variants cluster into two major categories: 1) those that disrupt polycystin cell surface localization or 2) those that attenuate polycystin ion channel activity. We found that polycystin channels with defective surface localization could be rescued with a small molecule. We propose that small-molecule-based strategies to improve polycystin cell surface localization and channel function will be effective therapies for ADPKD patients.

cell biology↗

bioRxiv2023

Cell-Specific Transposable Element Gene Expression Analysis Identifies Associations with Systemic Lupus Erythematosus Phenotypes

There is an established yet unexplained link between interferon (IFN) and systemic lupus erythematosus (SLE). The expression of sequences derived from transposable elements (TEs) may contribute to production of type I IFNs and generation of autoantibodies. We profiled cell-sorted RNA-seq data (CD4+ T cells, CD14+ monocytes, CD19+ B cells, and NK cells) from PBMCs of 120 SLE patients and quantified TE expression identifying 27,135 TEs. We tested for differential TE expression across 10 SLE phenotypes including autoantibody production and disease activity and discovered 731 differentially expressed (DE) TEs whose effects were mostly cell-specific and phenotype-specific. DE TEs were enriched for specific families and viral genes encoded in TE sequences. Increased expression of DE TEs was associated with genes involved in antiviral activity such as LY6E, ISG15, TRIM22 and pathways such as interferon signaling. These findings suggest that expression of TEs contributes to activation of SLE-related mechanisms in a cell-specific manner, which can impact disease diagnostics and therapeutics.

molecular biology↗

bioRxiv2023

Coordinated immune dysregulation in Juvenile Dermatomyositis revealed by single-cell genomics

Juvenile Dermatomyositis (JDM) is one of several childhood-onset autoimmune disorders characterized by a type I interferon response and autoantibodies. Treatment options are limited due to incomplete understanding of how the disease emerges from dysregulated cell states across the immune system. We therefore investigated the blood of JDM patients at different stages of disease activity using single-cell transcriptomics paired with surface protein expression. By immunophenotyping peripheral blood mononuclear cells, we observed skewing of the B cell compartment towards an immature naive state as a hallmark of JDM. Furthermore, we find that these changes in B cells are paralleled by signatures of Th2-mediated inflammation. Additionally, our work identified SIGLEC-1 expression in monocytes as a composite measure of heterogeneous type I interferon activity in disease. We applied network analysis to reveal that hyperactivation of the type I interferon response in all immune populations is coordinated with dysfunctional protein processing and regulation of cell death programming. This analysis separated the ubiquitously expressed type I interferon response into a central hub and revealed previously masked cell states. Together, these findings reveal the coordinated immune dysregulation underpinning JDM and provide novel insight into strategies for restoring balance in immune function.

immunology↗

bioRxiv2023

Deciphering Cell-types and Gene Signatures Associated with Disease Activity in Rheumatoid Arthritis using Single Cell RNA-sequencing

ObjectiveSingle cell profiling of synovial tissue has previously identified gene signatures associated with rheumatoid arthritis (RA) pathophysiology, but synovial tissue is difficult to obtain. This study leverages single cell sequencing of peripheral blood mononuclear cells (PBMCs) from patients with RA and matched healthy controls to identify disease relevant cell subsets and cell type specific signatures of disease. MethodsSingle-cell RNA sequencing (scRNAseq) was performed on peripheral blood mononuclear cells (PBMCs) from 18 RA patients and 18 matched controls, accounting for age, gender, race, and ethnicity). Samples were processed using standard CellRanger and Scanpy pipelines, pseudobulk differential gene expression analysis was performed using DESeq2, and cell-cell communication analysis using CellChat. ResultsWe identified 18 distinct PBMC subsets, including a novel IFITM3+ monocyte subset. CD4+ T effector memory cells were increased in patients with moderate to high disease activity (DAS28-CRP [≥] 3.2), while non-classical monocytes were decreased in patients with low disease activity or remission (DAS28-CRP < 3.2). Differential gene expression analysis identified RA-associated genes in IFITM3+ and non-classical monocyte subsets, and downregulation of pro-inflammatory genes in the V{delta} subset. Additionally, we identified gene signatures associated with disease activity, characterized by upregulation of pro-inflammatory genes TNF, JUN, EGR1, IFIT2, MAFB, G0S2, and downregulation of HLA-DQB1, HLA-DRB5, TNFSF13B. Notably, cell-cell communication analysis revealed upregulation of immune-associated signaling pathways, including VISTA, in patients with RA. ConclusionsWe provide a novel single-cell transcriptomics dataset of PBMCs from patients with RA, and identify insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

immunology↗

bioRxiv2023

Reference-free multiplexed single-cell sequencing identifies genetic modifiers of the human immune response

Multiplexed single-cell sequencing (mux-seq) using single-nucleotide polymorphisms (SNPs) has emerged as an efficient approach to perform expression quantitative trait loci (eQTL) studies that map interactions between genetic variants and cell types, cell states, or experimental perturbations. Here we introduce the clue framework, a novel approach to encode mux-seq experiments that eliminates the need for reference genotypes and experimental barcoding. The clue framework is made possible by the development of freemuxlet, an algorithm that clusters cells based on SNPs called from single-cell RNA-seq or ATAC-seq data. To demonstrate the feasibility of clue, we profiled the surface protein and RNA abundances of peripheral blood mononuclear cells from 64 individuals, stimulated with 5 distinct extracellular stimuli -- all within a single day. Our analysis of the demultiplexed data identified rare immune cell types and cell type-specific responses to interferon and toll-like receptor stimulation. Furthermore, by integrating genotyping data, we mapped response eQTLs specific to certain cell types. These findings showcase the potential and scalability of the clue framework for reference-free multiplexed single-cell sequencing studies.

genomics↗

bioRxiv2023

Reply to Barton et al: signatures of natural selection during the Black Death

Barton et al.1 raise several statistical concerns regarding our original analyses2 that highlight the challenge of inferring natural selection using ancient genomic data. We show here that these concerns have limited impact on our original conclusions. Specifically, we recover the same signature of enrichment for high FST values at the immune loci relative to putatively neutral sites after switching the allele frequency estimation method to a maximum likelihood approach, filtering to only consider known human variants, and down-sampling our data to the same mean coverage across sites. Furthermore, using permutations, we show that the rs2549794 variant near ERAP2 continues to emerge as the strongest candidate for selection (p = 1.2x10-5), falling below the Bonferroni-corrected significance threshold recommended by Barton et al. Importantly, the evidence for selection on ERAP2 is further supported by functional data demonstrating the impact of the ERAP2 genotype on the immune response to Y. pestis and by epidemiological data from an independent group showing that the putatively selected allele during the Black Death protects against severe respiratory infection in contemporary populations.

genomics↗

bioRxiv2023

Mitigation of chromosome loss in clinical CRISPR-Cas9-engineered T cells

CRISPR-Cas9 genome editing has enabled advanced T cell therapies, but occasional loss of the targeted chromosome remains a safety concern. To investigate whether Cas9-induced chromosome loss is a universal phenomenon and evaluate its clinical significance, we conducted a systematic analysis in primary human T cells. Arrayed and pooled CRISPR screens revealed that chromosome loss was generalizable across the genome and resulted in partial and entire loss of the chromosome, including in pre-clinical chimeric antigen receptor T cells. T cells with chromosome loss persisted for weeks in culture, implying the potential to interfere with clinical use. A modified cell manufacturing process, employed in our first-in-human clinical trial of Cas9-engineered T cells,1 dramatically reduced chromosome loss while largely preserving genome editing efficacy. Expression of p53 correlated with protection from chromosome loss observed in this protocol, suggesting both a mechanism and strategy for T cell engineering that mitigates this genotoxicity in the clinic.

cell biology↗

bioRxiv2022

Uncovering context-specific genetic-regulation of gene expression from single-cell RNA-sequencing using latent-factor models

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

bioinformatics↗

bioRxiv2022

memento: Generalized differential expression analysis of single-cell RNA-seq with method of moments estimation and efficient resampling

Differential expression analysis of scRNA-seq data is central for characterizing how experimental factors affect the distribution of gene expression. However, it remains challenging to distinguish biological and technical sources of cell-cell variability and to assess the statistical significance of quantitative comparisons between groups of cells. We introduce memento to address these limitations and enable accurate and efficient differential expression analysis of the mean, variability, and gene correlation from scRNA-seq. We used memento to analyze 70,000 tracheal epithelial cells to identify interferon response genes with distinct variability and correlation patterns, 160,000 T cells perturbed with CRISPR-Cas9 to reconstruct gene-regulatory networks that control T cell activation, and 1.2 million PMBCs to map cell-type-specific cis expression quantitative trait loci (eQTLs). In all cases, memento identified more significant and reproducible differences in mean expression but also identified differences in variability and gene correlation that suggest distinct modes of transcriptional regulation imparted by cytokines, genetic perturbations, and natural genetic variation. These results demonstrate memento as a first-in-class method for the quantitative comparisons of scRNA-seq data scalable to millions of cells and thousands of samples.

bioinformatics↗

bioRxiv2022

Cell-subtype specific effects of genetic variation in the aging and Alzheimer cortex

The relationship between genetic variation and gene expression in individual brain cell types and subtypes has remained elusive. Here, we generated single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex of 424 individuals of advanced age; analyzing 1.5 million nuclear transcriptomes, we assessed the effect of genetic variants on RNA expression in cis (cis-eQTL) for 7 cell types and 81 cell subtypes. This effort identified 10,004 eGenes at the cell type level and 8,138 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer pathology, accounting for the effect of APOE{varepsilon}4, providing mechanistic insights into both pathologies. While eQTLs are readily detected, only a TMEM106B variant robustly affects the proportion of cell subtypes. Integration of these results with GWAS highlighted the targeted cell type and likely causal gene within susceptibility loci for Alzheimers, Parkinsons, schizophrenia, and educational attainment.

genomics↗

bioRxiv2022

Genome-wide prediction of disease variants with a deep protein language model

Distinguishing between damaging and neutral missense variants is an ongoing challenge in human genetics, with profound implications for clinical diagnosis, genetic studies and protein engineering. Recently, deep-learning models have achieved state-of-the-art performance in classifying variants as pathogenic or benign. However, these models are currently unable to provide predictions over all missense variants, either because of dependency on close protein homologs or due to software limitations. Here we leveraged ESM1b, a 650M-parameter protein language model, to predict the functional impact of human coding variation at scale. To overcome existing technical limitations, we developed a modified ESM1b workflow and functionalized, for the first time, all proteins in the human genome, resulting in predictions for all [~]450M possible missense variant effects. ESM1b was able to distinguish between pathogenic and benign variants across [~]150K variants annotated in ClinVar and HGMD, outperforming existing state-of-the-art methods. ESM1b also exceeded the state of the art at predicting the experimental results of deep mutational scans. We further annotated [~]2M variants across [~]9K alternatively-spliced genes as damaging in certain protein isoforms while neutral in others, demonstrating the importance of considering all isoforms when functionalizing variant effects. The complete catalog of variant effect predictions is available at: https://huggingface.co/spaces/ntranoslab/esm_variants.

bioinformatics↗

bioRxiv2022

Modular Pooled Discovery of Synthetic Knockin Sequences to Program Durable Cell Therapies

Chronic stimulation can cause T cell dysfunction and limit efficacy of cellular immunotherapies. CRISPR screens have nominated gene targets for engineered T cells, but improved methods are required to compare large numbers of synthetic knockin sequences to reprogram cell functions. Here, we developed Modular Pooled Knockin Screening (ModPoKI), an adaptable platform for modular construction of DNA knockin libraries using barcoded multicistronic adaptors. We built two ModPoKI libraries of 100 transcription factors (TFs) and 129 natural and synthetic surface receptors. Over 20 ModPoKI screens across human TCR and CAR T cells in diverse conditions identified a transcription factor AP4 (TFAP4) construct to enhance long-term T cell fitness and anti-cancer function in vitro and in vivo. ModPoKIs modularity allowed us to generate a [~]10,000-member library of TF combinations. Non-viral knockin of a combined BATF-TFAP4 polycistronic construct further enhanced function in vivo. ModPoKI facilitates discovery of complex gene constructs to program cellular functions. HighlightsO_LIModular pooled knockins of hundreds of TF and surface receptor constructs combined with different antigen receptors C_LIO_LIChronic stimulation screens discover programs to improve T cell persistence C_LIO_LICombinatorial knockin screens with [~]10,000 transcription factor combinations C_LIO_LIBATF-TFAP4 dual knockin construct improves CAR T cell function in vitro and in vivo C_LI

immunology↗

bioRxiv2022

Systematic functional interrogation of SARS-CoV-2 host factors using Perturb-seq

Genomic and proteomic screens have identified numerous host factors of SARS-CoV-2, but efficient delineation of their molecular roles during infection remains a challenge. Here we use Perturb-seq, combining genetic perturbations with a single-cell readout, to investigate how inactivation of host factors changes the course of SARS-CoV-2 infection and the host response in human lung epithelial cells. Our high-dimensional data resolve complex phenotypes such as shifts in the stages of infection and modulations of the interferon response. However, only a small percentage of host factors showed such phenotypes upon perturbation. We further identified the NF-{kappa}B inhibitor I{kappa}B (NFKBIA), as well as the translation factors EIF4E2 and EIF4H as strong host dependency factors acting early in infection. Overall, our study provides massively parallel functional characterization of host factors of SARS-CoV-2 and quantitatively defines their roles both in virus-infected and bystander cells.

systems biology↗

bioRxiv2022

Persistent postnatal migration of interneurons into the human entorhinal cortex

The entorhinal cortex (EC) is a highly-interconnected hub for multisensory integration and memory processing1-3, containing diverse neuronal subtypes4,5 including subpopulations that are uniquely spatially-tuned6,7. Although many spatial and memory functions develop in infancy, it is considered that neurogenesis and neuronal migration to the EC occurs prenatally. Here we show that the postnatal human temporal lobe contains a prominent stream with large chains of young migrating neurons and many individual neurons breaking away directed into the EC. The EC stream forms between the second and third trimesters of prenatal development when the lateral ventricle walls in the temporal lobe collapse, displacing the subventricular zone (SVZ) and dividing radial glia. At birth, the EC stream follows a path of radial glial [fi]bers in the site of the collapsed ventricle. Migratory chains persist up to 11 months postnatally; however, many individually migrating young neurons can still be detected in the EC at 2 years of age and a few isolated cells at 3 years of age. Within the EC at birth, immature neurons are a mixed population expressing markers of the medial ganglionic eminence (MGE) and caudal ganglionic eminence (CGE), but postnatally rapidly become primarily CGE-derived. Using single-nuclei RNAseq we identified these lineages and found that the MGE-derived neurons matured at earlier postnatal ages compared to those derived from the CGE. The CGE interneurons arriving and maturing the latest included subtypes expressing calretinin (CR), reelin (RELN), and vasoactive intestinal protein (VIP) many of which settle in layer II of the entorhinal cortex. This study reveals that the human EC is still being constructed during the first years of life revealing the largest known postnatal stream of migratory neurons in humans. The protracted postnatal arrival of a diverse population of interneurons could contribute to plasticity8,9 and proper excitation-inhibition balance10,11 within these highly connected brain circuits.

neuroscience↗

bioRxiv2022

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

Tissue- and organism-level biological processes often involve coordinated action of multiple distinct cell types. Current computational methods for the analysis of single-cell RNA-sequencing (scRNA-seq) data, however, are not designed to capture co-variation of cell states across samples, in part due to the low number of biological samples in most scRNA-seq datasets. Recent advances in sample multiplexing have enabled population-scale scRNA-seq measurements of tens to hundreds of samples. To take advantage of such datasets, here we introduce a computational approach called single-cell Interpretable Tensor Decomposition (scITD). This method extracts "multicellular gene expression patterns" that capture how sample-specific expression states of a cell type are correlated with the expression states of other cell types. Such multicellular patterns can reveal molecular mechanisms underlying coordinated changes of different cell types within the tissue, and can be used to stratify individuals in a clinically-relevant and reproducible manner. We first validated the performance of scITD using in vitro experimental data and simulations. We then applied scITD to scRNA-seq data on peripheral blood mononuclear cells (PBMCs) from 115 patients with systemic lupus erythematosus and 56 healthy controls. We recapitulated a well-established pan-cell-type signature of interferon-signaling that was associated with the presence of anti-dsDNA autoantibodies and a disease activity index. We further identified a novel multicellular pattern linked to nephritis, which was characterized by an expansion of activated memory B cells along with helper T cell activation. Our approach also sheds light on ligand-receptor interactions potentially mediating these multicellular patterns. As validation, we demonstrated that these expression patterns also stratified donors from a pediatric SLE dataset by the same phenotypic attributes. Lastly, we found the interferon multicellular pattern and others to be conserved in a COVID-19 dataset, pointing to the presence of both general and disease-specific patterns of inter-individual immune variation. Overall, scITD is a flexible method for exploring co-variation of cell states in multi-sample single-cell datasets, which can yield new insights into complex non-cell-autonomous dependencies that define and stratify disease.

bioinformatics↗

bioRxiv2022

Naive arthritogenic SKG T cells have a defect in anergy and a repertoire pruned by superantigen

How autoreactive CD4 T cells develop to cause rheumatoid arthritis remains unknown. We used a reporter for antigen-receptor signaling in the SKG autoimmune arthritis model to profile a T cell subpopulation enriched for arthritogenic naive CD4 T cells before arthritis onset by bulk and single cell RNA and T cell antigen-receptor (TCR) sequencing. Our analyses reveal that despite their impaired proximal TCR signaling, a subset of SKG naive CD4 T cells that have recently encountered endogenous antigen upregulate gene programs associated with positive regulation of T cell activation and cytokine signaling at higher levels than wild type cells in the pre-disease state. These arthritogenic cells also induce genes associated with negative regulation of T cell activation but do so less efficiently than wild type cells. Furthermore, their TCR sequences exhibit a previously unrecognized biased peripheral TCR V{beta} repertoire likely driven by endogenous viral superantigens. These particular V{beta}s, known to recognize endogenous mouse mammary tumor virus (MMTV) superantigen, are further expanded in arthritic joints. Our results demonstrate that autoreactive naive CD4 T cells which recognize endogenous viral superantigens are poised to cause disease by their altered transcriptome. Summary blurbSelf-reactive SKG T cells that escaped negative selection harbor an independent defect in anergy that, together with chronic antigen stimulation, sets the stage for disease. Moreover, we propose a novel role for endogenous mouse mammary tumor virus (MMTV) superantigen in promoting arthritogenic T cell responses.

immunology↗

bioRxiv2021

Multi-context genetic modeling of transcriptional regulation resolves novel disease loci

A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We developed CONTENT-- a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We applied CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. Interestingly, we find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increased the number of gene-phenotype associations discovered by over 47% relative to previous methods across 22 complex traits.

genomics↗

bioRxiv2021

Pooled screening of CAR T cells identifies non-native signaling domains for next- generation immunotherapies

Chimeric antigen receptors (CARs) repurpose natural signaling components to retarget T cells to refractory cancers, but have shown limited efficacy against solid tumors. Here, we introduce CAR Pooling, a multiplexed approach to rapidly identify CAR designs with clinical potential. Forty CARs with diverse immune costimulatory domains were assessed in pooled assays for their ability to stimulate critical T cell effector functions during repetitive stimulation that mimics long-term tumor antigen exposure. Several non-native domains from the TNF receptor family exhibited enhanced proliferation (CD40) or cytotoxicity (BAFF-R and TACI) relative to clinical benchmarks, and fell into distinct states of memory, cytotoxicity, and metabolism. BAFF-R CAR T cells were enriched for a highly cytotoxic and NK-cell-like innate phenotype previously associated with positive clinical outcomes. CAR Pooling enables efficient exploration of how CAR design affects cell activity and can be applied to optimize receptors across a range of applications and cell types.

immunology↗