单细胞组学及其应用（英文版）.pptx

单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,2021/5/24,#,Single Cell Omics,and Its Application,Content,Overview,Next,Generation Single Cell Analysis,Single cell DNA sequencing and its application,Single cell RNA sequencing and its,application,Applications on CTC,3,The front-end and back-end of single cell analysis,Cell isolation,Omics tech,Microfuidics,Microscopic capture/micromanipulation,4,What is Single Cell Omics?,Front-End,Disadvantage:,Throughput;,Resolution;,Advantage:,Easy to access;,Easy to use;,Fluorescence Activated Cell Sorting(FACS),5,What is Single Cell Omics?,Front-End,Disadvantage:,Cell damage;,Fluoresce affect amplification,Advantage:,High throughput;,Specific markers;,Laser capture microdissection/laser microdissection,6,What is Single Cell Omics?,Front-End,Disadvantage:,Cell damage and information loss;,Throughput;,Advantage:,Micro-environment;,Specific markers and specific region,Microfluidics:,7,What is Single Cell Omics?,Front-End,Disadvantage:,User friendly?,Modulated?,Advantage:,High throughput;,Automatic,8,What is Single Cell Omics?,What is Single Cell Omics?,Bridging,Fluidigm C1 System,MIRALCS System,Single cell DNA sequencing and its application,Single Cell Genomics,Samples,Single Cell,NGS,Cell isolation,WGA,Sequencing the Single Cell Genome by Next Generation Sequencing(NGS),Library Preparation,Enough Amount of DNA,Bioinformatics Analysis,12,Single,Cell,G,enomics,Research,13,Whole Genome Amplification(WGA),(Zong,2012),Single Cell Genomics,Multiple Displacement Amplification(MDA),Multiple,Annealing and Looping Based Amplification Cycles(MALBAC),No,obvious genome wide coverage limitation by single cell sequencing,14,Methodology,of Single Cell Sequencing,GC content does impact the even distribution of WGA data.,Acceptable sequencing error,rate,FN,&FDR sites do not show specific base type bias beyond,mutations,Amplification failure genes do not show preference on different biological,processes,Sample Description,Tumor type,Single cell number,tumor cell#/control cell#(sequencing available number),Gender,Description,Essential thrombocytosis(ET),81/9(58/8),M,a,JAK2,-negative myeloproliferative neoplasm patient;,published on,Cell,2012,Clear cell renal cell cancer(ccRcc),20/6(20/5),M,a V,HL-wild type patient;,published on,Cell,2012,Bladder transitional cell cancer(BTCC),55/11(44/11),M,a muscle-invasive type patient;,published on,Giga Science,2012,Single Cell Sequencing of Primary,Tumors,Notes:All refer to target region of Agilent.We also sequenced the normal tissue(100X exome)or peripheral blood cells(30X exome),and,tumor,tissues(100X exome)to make quality control.,Sample,Depth(,SEM),Coverage(,SEM),Used tumor cell#/normal cells#,ET,33.80,2.73,86.63%,2.01%,58/8,ccRcc,36.12,3.06,91.19%,1.93%,20/5,BTCC,45.952.71,88.60%1.40%,44/11,Data Sets for single-cell exome sequencing,16,Single Cell Sequencing of Primary,Tumors,Somatic mutation calling,Population analysis,Progression inferring(key genes),Analysis pipeline,Observe the somatic mutation pattern in single cell level,Somatic mutation statistics,Derived allelic frequency spectrum of somatic mutations,Mutation prevalence of single cell level in different tumors,Somatic mutation types of single cell level in different tumors,Tumor-mutated genes,Functional validation,Single Cell Sequencing of Primary,Tumors,17,Somatic mutation statistics,Sample,Silent,Missense,Nonsense,Non-syn/syn,ET,93,76,2,0.84,ccRcc,24,90,6,4.00,BTCC,59,136,10,2.47,An,increasing percentage of,transversion,mutations.,Applying to Primary Tumor Research,Somatic mutant allele frequency correlation between single cell data and the millions of cells control,is high,19,ET,ccRCC,Applying to Primary Tumor Research,BTCC,Heterogeneity:SCS distinguishes tumor cells with normal cells and with themselves,Principle component analysis(PCA),distinguish tumor and,normal cells,apparently,20,ccRCC,Applying to Primary Tumor Research,BTCC,ET,Phylogenetic tree of ccRCC cells,Comparing the simulation of somatic mutated allele frequency and our in silico data shows the potential monoclonal progression of this disease.,Evolution:,SCS,reveals monoclonal progression of ET,21,Applying to Primary Tumor Research,Driver gene prediction of the non-synonymous somatic mutations:Q-score was calculated according to a modified method by,(Youn and Simon,2011).Genes with Q-score more than 1 were identified as key genes.,Ke,y mutations:,key mutations identified by SCS in ET based on,in silico,prediction,22,Applying to Primary Tumor Research,Key genes with known function and correlation with cancer.,23,Key mutations:key mutations identified by SCS in ET based on,in silico,prediction,Applying to Primary Tumor Research,Mutated genes landscape:mountain(tissue common mutation,fre20%)and hill(cell specific mutation)genes;,24,Key mutations:key mutations identified by SCS in ccRCC based on comparison with patient cohort study,Applying to Primary Tumor Research,Key mutations:key mutations identified by SCS in,ccRCC,based on,comp。