Biology subjects

Dou, J.

Publications and source records attributed to Dou, J..

33 records · Page 2Linked to original sources

bioRxiv2021

Systematic decomposition of sequence determinants governing CRISPR/Cas9 specificity

The specificity of CRISPR/Cas9 genome editing is largely determined by the sequences of guide RNA (gRNA) and the targeted DNA, yet the sequence-dependent rules underlying off-target effects are not fully understood. Here we systematically investigated the sequence determinants governing CRISPR/Cas9 specificity by measuring the off-on ratios of 1,902 gRNAs on 13,314 target sequences using an improved synthetic system with dual-target design. Our study revealed a comprehensive set of rules including 3 factors in CRISPR/Cas9 off-targeting: 1) the nucleotide context and position of a single mismatch; 2) an "epistasis-like" combinatorial effect of multiple mismatches; and 3) a guide-intrinsic mismatch tolerance (GMT) independent of the mismatch context. Notably, the combinatorial effect and GMT are associated with the free-energy landscape in R-loop formation and are explainable by a multi-state kinetic model. Based on these rules, we developed a model-based off-target prediction tool (MOFF), which showed superior performance compared to the existing methods.

systems biology↗

bioRxiv2021

Placental gene expression-based cell type deconvolution: Cell proportions drive preeclampsia gene expression differences

The placenta mediates adverse pregnancy outcomes, including preeclampsia, which is characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue at term (n=15,532 cells). We deconvoluted eight published microarray case-control studies of preeclampsia (n=330). Deconvolution revealed excess extravillous trophoblasts and fewer mesenchymal cells. Adjustment for cellular composition reduced preeclampsia-associated differentially expressed genes (FDR<0.05) from 1,224 to 0, whereas pathway alterations exhibiting a metabolic adaptation to hypoxia were robust to cell type adjustment. Cellular composition explained 35.1% of the association between preeclampsia and FLT1 overexpression. Our findings indicate substantial placental cellular heterogeneity in preeclampsia that predicts previously observed bulk gene expression differences. Our deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.

developmental biology↗

bioRxiv2020

Single-Cell Manifold Preserving Feature Selection (SCMER)

1AO_SCPLOWBSTRACTC_SCPLOWA key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations (RCPs) that drive development, differentiation, and transformation. Molecular features such as genes and proteins defining RCPs are often unknown and difficult to detect from unenriched single-cell data, using conventional dimensionality reduction and clustering-based approaches. Here, we propose a novel unsupervised approach, named SCMER, which performs UMAP style dimensionality reduction via selecting a compact set of molecular features with definitive meanings. We applied SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis, and drug resistance and response. We found that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states ignored by current approaches. SCMER can be widely used for discovering novel molecular features in a high dimensional dataset, designing targeted, cost-effective assays for clinical applications, and facilitating multi-modality integration.

bioinformatics↗

bioRxiv2020

Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. Batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on a simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.

bioinformatics↗

bioRxiv2020

Generation of glucocorticoid resistant SARS-CoV-2 T-cells for adoptive cell therapy

Adoptive cell therapy with viral-specific T cells has been successfully used to treat life-threatening viral infections, supporting the application of this approach against COVID-19. We expanded SARS-CoV-2 T-cells from the peripheral blood of COVID-19-recovered donors and non-exposed controls using different culture conditions. We observed that the choice of cytokines modulates the expansion, phenotype and hierarchy of antigenic recognition by SARS-CoV-2 T-cells. Culture with IL-2/4/7 but not other cytokine-driven conditions resulted in >1000 fold expansion in SARS-CoV-2 T-cells with a retained phenotype, function and hierarchy of antigenic recognition when compared to baseline (pre-expansion) samples. Expanded CTLs were directed against structural SARS-CoV-2 proteins, including the receptor-binding domain of Spike. SARS-CoV-2 T-cells could not be efficiently expanded from the peripheral blood of non-exposed controls. Since corticosteroids are used for the management of severe COVID-19, we developed an efficient strategy to inactivate the glucocorticoid receptor gene (NR3C1) in SARS-CoV-2 CTLs using CRISPR-Cas9 gene editing.

immunology↗

bioRxiv2020

Ab initio Spillover Compensation in CyTOF Data

Signal intensity measured in a mass cytometry (CyTOF) channel can often be affected by the neighboring channels due to technological limitations. Such signal artifacts are known as spillover effects and can substantially limit the accuracy of cell population clustering. Current approaches reduce these effects by using additional beads for normalization purposes known as single-stained controls. While effective in compensating for spillover effects, incorporating single-stained controls can be costly and require customized panel design. This is especially evident when executing large-scale immune profiling studies. We present a novel statistical method, named CytoSpill that independently quantifies and compensates the spillover effects in CyTOF data without requiring the use of single-stained controls. Our method utilizes knowledge-guided modeling and statistical techniques, such as finite mixture modeling and sequential quadratic programming, to achieve optimal error correction. We evaluated our method using five publicly available CyTOF datasets obtained from human peripheral blood mononuclear cells (PBMCs), C57BL/6J mouse bone marrow, healthy human bone marrow, chronic lymphocytic leukemia patient, and healthy human cord blood samples. In the PBMCs with known ground truth, our method achieved comparable results to experiments that incorporated single-stained controls. In datasets without ground-truth, our method not only reduced spillover on likely affected markers, but also led to the discovery of potentially novel subpopulations expressing functionally meaningful, cluster-specific markers. CytoSpill (developed in R) will greatly enhance the execution of large-scale cellular profiling of tumor immune microenvironment, development of novel immunotherapy, and the discovery of immune-specific biomarkers. The implementation of our method can be found at https://github.com/KChen-lab/CytoSpill.git.

bioinformatics↗

bioRxiv2020

Sensei: How many samples to tell evolution in single-cell studies?

1AO_SCPLOWBSTRACTC_SCPLOWCellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

bioinformatics↗

bioRxiv2020

Single-cell copy number lineage tracing enabling gene discovery

Aneuploidy plays critical roles in genome evolution. Alleles, whose dosages affect the fitness of an ancestor, will have altered frequencies in the descendant populations upon perturbation. Single-cell sequencing enables comprehensive genome-wide copy number profiling of thousands of cells at various evolutionary stage and lineage. That makes it possible to discover dosage effects invisible at tissue level, provided that the cell lineages can be accurately reconstructed. Here, we present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles. We also present a statistical routine named lineage speciation analysis (LSA), which facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. We assessed our approaches using a variety of single-cell datasets. Overall, MEDALT appeared more accurate than phylogenetics approaches in reconstructing copy number lineage. From the single-cell DNA-sequencing data of 20 triple-negative breast cancer patients, our approaches effectively prioritized genes that are essential for breast cancer cell fitness and are predictive of patient survival, including those implicating convergent evolution. Similar benefits were observed when applying our approaches on single-cell RNA sequencing data obtained from cancer patients. The source code of our study is available at https://github.com/KChen-lab/MEDALT.

bioinformatics↗

bioRxiv2020

Inhibition of the av integrin-TGF-b axis improves natural killer cell function against glioblastoma stem cells

Glioblastoma, the most aggressive brain cancer, often recurs because glioblastoma stem cells (GSCs) are resistant to all standard therapies. Here, we show that patient-derived GSCs, but not normal astrocytes, are highly sensitive to lysis by healthy allogeneic natural killer (NK) cells in vitro. In contrast, single cell analysis of autologous, tissue infiltrating NK cells isolated from surgical samples of high-grade glioblastoma patient tumors using mass cytometry and single cell RNA sequencing revealed an abnormal phenotype associated with impaired lytic function compared with peripheral blood NK cells from GBM patients or healthy donors. This immunosuppression was attributed to an integrin-TGF-{beta} mechanism, activated by direct cell-cell contact between GSCs and NK cells. Treatment of GSC-engrafted mice with allogeneic NK cells in combination with inhibitors of integrin or TGF-{beta} signaling, or with TGF-{beta} receptor 2 gene-edited NK cells prevented GSC-induced NK cell dysfunction and tumor growth. Collectively, our findings reveal a novel mechanism of NK cell immune evasion by GSCs and implicate the integrin-TGF-{beta} axis as a useful therapeutic target to eliminate GSCs in this devastating tumor.

immunology↗

bioRxiv2019

Single cell analysis of the effects of developmental lead (Pb) exposure on the hippocampus.

BackgroundLead (Pb) exposure is ubiquitous and has permanent developmental effects on childhood intelligence and behavior and adulthood risk of dementia. The hippocampus is a key brain region involved in learning and memory, and its cellular composition is highly heterogeneous. Pb acts on the hippocampus by altering gene expression, but the cell type-specific responses are unknown. ObjectiveExamine the effects of perinatal Pb treatment on adult hippocampus gene expression, at the level of individual cells, in mice. MethodsIn mice perinatally exposed to control water (n=4) or a human physiologically-relevant level (32 ppm in maternal drinking water) of Pb (n=4), two weeks prior to mating through weaning, we tested for gene expression and cellular differences in the hippocampus at 5-months of age. Analysis was performed using single cell RNA-sequencing of 5,258 cells from the hippocampus by 10x Genomics Chromium to 1) test for gene expression differences averaged across all cells by treatment; 2) compare cell cluster composition by treatment; and 3) test for gene expression and pathway differences within cell clusters by treatment. ResultsGene expression patterns revealed 12 cell clusters in the hippocampus, mapping to major expected cell types (e.g. microglia, astrocytes, neurons, oligodendrocytes). Perinatal Pb treatment was associated with 12.4% more oligodendrocytes (P=4.4x10-21) in adult mice. Across all cells, differential gene expression analysis by Pb treatment revealed cluster marker genes. Within cell clusters, differential gene expression with Pb treatment (q<0.05) was observed in endothelial, microglial, pericyte, and astrocyte cells. Pathways up-regulated with Pb treatment were protein folding in microglia (P=3.4x10-9) and stress response in oligodendrocytes (P=3.2x10-5). ConclusionBulk tissue analysis may be confounded by changes in cell type composition and may obscure effects within vulnerable cell types. This study serves as a biological reference for future single cell studies of toxicant or neuronal complications, to ultimately characterize the molecular basis by which Pb influences cognition and behavior.

pharmacology and toxicology↗

bioRxiv2019

Muscle function and homeostasis require macrophage-derived cytokine inhibition of AKT activity in Drosophila

Unpaired ligands are secreted signals that act via a GP130-like receptor, domeless, to activate JAK-STAT signaling in Drosophila. Like many mammalian cytokines, unpaireds can be activated by infection and other stresses and can promote insulin resistance in target tissues. However, the importance of this effect in non-inflammatory physiology is unknown. Here, we identify a requirement for unpaired-JAK signaling as a metabolic regulator in healthy adult Drosophila muscle. Adult muscles show basal JAK-STAT signaling activity in the absence of any immune challenge. Macrophages are the source of much of this tonic signal. Loss of the dome receptor on adult muscles significantly reduces lifespan and causes local and systemic metabolic pathology. These pathologies result from hyperactivation of AKT and consequent deregulation of metabolism. Thus, we identify a cytokine signal from macrophages to muscle that controls AKT activity and metabolic homeostasis.

developmental biology↗

bioRxiv2019

De novo Identification of Essential Protein Domains from CRISPR/Cas9 Tiling-sgRNA Knockout Screens

High-throughput CRISPR/Cas9 knockout screens using a tiling-sgRNA design permit in situ evaluation of protein domain function. To facilitate de novo identification of essential protein domains from such screens, we developed ProTiler, a computational method for the robust mapping of CRISPR knockout hyper-sensitive (CKHS) regions, which refers to the protein regions that are associated with strong sgRNA dropout effect in the screens. We used ProTiler to analyze a published CRISPR tiling screen dataset, and identified 175 CKHS regions in 83 proteins. Of these CKHS regions, more than 80% overlapped with annotated Pfam domains, including all of the 15 known drug targets in the dataset. ProTiler also revealed unannotated essential domains, including the N-terminus of the SWI/SNF subunit SMARCB1, which we validated experimentally. Surprisingly, the CKHS regions were negatively correlated with phosphorylation and acetylation sites, suggesting that protein domains and post-translational modification sites have distinct sensitivities to CRISPR/Cas9 mediated amino acids loss.

bioinformatics↗

bioRxiv2018

Large-scale whole-genome sequencing of three diverse Asian populations in Singapore

Asian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups: 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7x, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with <0.1% allele frequency. We found 89.2 million single-nucleotide polymorphisms (SNPs) and 9.1 million small insertions and deletions (INDELs), more than half of which have not been cataloged in dbSNP. In particular, we found 126 common deleterious mutations (MAF>0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.

genetics↗

bioRxiv2018

DNA methylation signature of smoking in lung cancer is enriched for exposure signatures in newborn and adult blood.

BackgroundSmoking impacts DNA methylation genome-wide in blood of both newborns from maternal smoking during pregnancy and adults from personal smoking. Smoking causes lung cancer which involves aberrant methylation. We examined whether DNA methylation smoking signatures identified in blood of newborns and adults are detectable in lung tumors.\n\nMethodsWe compared smoking-related DNA methylation in lung adenocarcinomas (61 never smokers, 91 current smokers, and 238 former smokers) quantified with the Illumina450k BeadArray in The Cancer Genome Atlas with published large consortium meta-analyses of newborn and adult blood. We assessed whether CpG sites related to smoking in blood from newborns and adults were enriched in lung adenocarcinoma.\n\nResultsTesting CpGs differentially methylated by smoke exposure (P<10-4) we identified 296 in lung tumors, while previous meta-analyses (False Discovery Rate (FDR)<0.05) identified 6,073 in newborn blood, and for adult smoking, 18,760 in blood. The lung signals were highly enriched for those seen in newborn (32 overlapping, Penrichment=1.2x10-19) and adult blood (86 overlapping, Penrichment = 9.5x10-49). The 65 genes annotated to CpGs differentially methylated in lung tumors, but not blood, were enriched for RNA processing ontologies.\n\nConclusionsWe found highly significant overlap between smoking-related DNA methylation signals in lung cancer and those seen in blood from newborns, from in utero exposure, or adults, from their own exposure. These results suggest that some epigenetic alterations associated with cigarette smoke exposure are tissue specific, but others are common across tissues. These findings support the value of blood-based methylation biomarkers for assessing exposure effects in target tissues.

cancer biology↗

bioRxiv2017

Cord blood buffy coat DNA methylation is comparable to whole cord blood methylation

BackgroundCord blood DNA methylation is associated with numerous health outcomes and environmental exposures. Whole cord blood DNA reflects all nucleated blood cell types, while centrifuging whole blood separates red blood cells by generating a white blood cell buffy coat. Both sample types are used in DNA methylation studies. Cell types have unique methylation patterns and processing can impact cell distributions, which may influence comparability.\n\nObjectivesTo evaluate differences in cell composition and DNA methylation between buffy coat and whole cord blood samples.\n\nMethodsCord blood DNA methylation was measured with the Infinium EPIC BeadChip (Illumina) in 8 individuals, each contributing buffy coat and whole blood samples. We analyzed principal components (PC) of methylation, performed hierarchical clustering, and computed correlations of mean-centered methylation between pairs. We conducted moderated t-tests on single sites and estimated cell composition.\n\nResultsDNA methylation PCs were associated with individual (PPC1=1.4x10-9; PPC2=2.9x10-5; PPC3=3.8x10-5; PPC4=4.2x10-6; PPC5=9.9x10-13), and not with sample type (PPC1-5>0.7). Samples hierarchically clustered by individual. Pearson correlations of mean-centered methylation between paired individual samples ranged from r=0.66 to r=0.87. No individual site significantly differed between buffy coat and whole cord blood when adjusting for multiple comparisons (5 sites had unadjusted P<10-5). Estimated cell type proportions did not differ by sample type (P=0.86), and estimated cell counts were highly correlated between paired samples (r=0.99).\n\nConclusionsDifferences in methylation and cell composition between buffy coat and whole cord blood are much lower than inter-individual variation, demonstrating that both sample preparation types can be analytically combined and compared.

epidemiology↗