Program of Single Cell Omics Beijing 2022.

Notes from Program of Single Cell Omics Beijing 2022.

• Opening
• Session 1
  • Bing Ren: Illuminating the Dark Matter in Human DNA with Single-cell Epigenomics Analysis
  • Jay Shendure: Reconstruction & Recording of Mammalian Development
• Session 2
  • Bart Deplancke: Engineering Next-generation Single Cell Phenomics Technologies
  • Rickard Sandberg: Scalable Full-length scRNA-seq for Temporal Analyses of Transcription and Dissections of Cell States and Subtypes
  • Angela Ruohao Wu: Multi-step Single-cell Multi-omics Methods for Simultaneous Dissection of Phenotype and Genotype Heterogeneity from Frozen Tumors
  • Guoji Guo:Mapping Cell Landscapes at Single cell Level
  • Chenghang Zong: Total-RNA Based scRNA-seq Allows Genome-wide Identification of Transcriptional and Post-transcriptional Regulation
• Section 3
  • Ning Jenny Jiang: High-throughput and High-Dimensional Single T Cell Profiling
  • Yanyi Huang: Improving the Information Efficiency for Fast and Spatially Resolved Sequencing
  • Fuchou Tang: Single Cell Omics Sequencing Technologies: The Next Generation
  • Zemin Zhang: Dynamic Changes of The Tumor Micro-environment During Immunotherapies
  • Xiaoqun Wang: Spatial Mutli-omics Sequencing the Developing Human Cerebellum
• Session 4
  • Alexander van Oudenaarden: Acceleration of Genome Replication Uncovered by Single-cell Nascent DNA Sequencing
  • David Weitz: Applications of Single Microbe Sequencing
  • Amos Tanay: Single Cell Models for Deciphering the Birth of Cell-type Specific Epigenetics During Gastrulation
  • Ge Gao: Rationally Design Generative Models for Delineating the Regulator Map in silico

Oct. 13-14, 2022. Beijng China

Opening

Session 1

Moderator: Xiaoliang Sunney Xie

Bing Ren: Illuminating the Dark Matter in Human DNA with Single-cell Epigenomics Analysis

• background: “risk variants associated with gene expression regulation”

• Method development (sci-based):

  • Experimental: snATAC-Seq, snMethyl-HiC, Paired-Tag/Seq

  • Computational: SnapATAC, SnapHiC

• Predicting disease-associated cell types

  Utilize GWAS data, analysis enrichment in cell types by intersecting with cCREs.

• Identify key regulatory elements associated with disease

• Summary:

• Q&A:
  • Associated cCREs with genes

Jay Shendure: Reconstruction & Recording of Mammalian Development

• background: development of single-cell methods

• scRNA-seq of ~2 million cells in one experiment (sc-based, $384 ^ 3$ )

• Epithelial development

Session 2

Moderator: Fuchou Tang

Bart Deplancke: Engineering Next-generation Single Cell Phenomics Technologies

• DisCo

• Live-seq

  • minor perturbation on target cells

  • sequential Live-seq: state transition of the same cell

  • transcriptomic recorder

Rickard Sandberg: Scalable Full-length scRNA-seq for Temporal Analyses of Transcription and Dissections of Cell States and Subtypes

• NASC-seq2: single-cell nascent RNA sequencing

  • Co-bursting: do nearby genes burst independently?

  • general independent transcription of two alleles

  • co-bursting outliers :

• Smart-seq3xpress: scalable, cost efficient

questions:

pseudo-gene expression and duplicated genes may contributed to co-bursting outliers. mentioned pseudo-gene expression. however, the investigation of pseduo-gene influenced by the high sequence similarity, specific steps to resolve this.

Angela Ruohao Wu: Multi-step Single-cell Multi-omics Methods for Simultaneous Dissection of Phenotype and Genotype Heterogeneity from Frozen Tumors

• Mouse Lemur Single Cell Atlas

• scONE-seq: one tube single-cell WGS and RNA-sequeicing

  • single-cell multi-omics is especially useful for cancer analysis.

  • existing single-cell DNA&RNA methods:

  • principles of scONE-seq

  • performance:

    RNA (total RNA):

    CNV:

  • discoveries:

    a novel tumor sub-clone in astrocytoma

    Tumor cells with minor difference on transcription identified from CNV information,

  • summary

Guoji Guo:Mapping Cell Landscapes at Single cell Level

• background: equation for cell fate decision

• Microwell-seq

• Microwell-seq 2.0

• human atlas
  • inflamed structural (endothelial, epithelial, stromal) cells, validated in vivo.

• mouse atlas

• lifespan cell landscape analysis

  • inflammation in structural cells
  • mitochondria metabolism

• how cell types are regulated?

  • TF dynamics
  • Cross-species analysis of common TFs during differentiation

• Nvwa

Chenghang Zong: Total-RNA Based scRNA-seq Allows Genome-wide Identification of Transcriptional and Post-transcriptional Regulation

• background: Total-RNA based single-cell RNA-seq

  chemistry of MATQ-seq:

• SMART-MATQ seq

  include intron (nascent), more appropriate for RNA velocity.

• characterize the cell dynamics in cell cycle

• differential gene expression along certain trajectory:

  • method: tradeSeq (2020, Nat commu)

  • compare DEG of mature and nascent RNAs defines 3 types of distinct cell cycle genes (CCGs)

  • Novel cell cycle genes

  • dynamic modules in different CCGs

Question:

• during CCG identification, the significance of type III CCGs? (nascent only) how to explain stochastic fluctuation or some biological mechanisms.
• why identify novel CCGs in type I? since type 1 could be detected by mature RNA alone.

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Section 3

Moderator: Yanyi Huang

Ning Jenny Jiang: High-throughput and High-Dimensional Single T Cell Profiling

• pMHC generation by IVTT

• TetTCF-seqHD

• performance on CD8+ T cells

• conclusions

Yanyi Huang: Improving the Information Efficiency for Fast and Spatially Resolved Sequencing

• ECC sequencing methods:

• bit-seq

Fuchou Tang: Single Cell Omics Sequencing Technologies: The Next Generation

• SCAN-seq: full-length scRNA-Seq
• SCAN-seq2: higher throughput
• SMOOTH-seq2: scWGS sequencing
• single-cell assembly

Zemin Zhang: Dynamic Changes of The Tumor Micro-environment During Immunotherapies

• background: tumor microenvironmnt

• composition: certain cell types

• heterogenity:

  • pan-cancer analysis of infiltrating myeloid cells

  • pan-cancer analysis of infiltrating T cells

• Temporal dynamics

  • dynamics of T cells

  • dynamics of LAMP3+ DCs in TAMs in HCC

• clinical relavance

  • responsive tumor showed decrease of terminal Tex

Xiaoqun Wang: Spatial Mutli-omics Sequencing the Developing Human Cerebellum

Session 4

Moderator: Yanyi Huang

Alexander van Oudenaarden: Acceleration of Genome Replication Uncovered by Single-cell Nascent DNA Sequencing

• question: measure velocity of genome replication

  • single-molecule methods

  • single-molecule measurement differs from single-cell measurement

• scEdUseq: single-cell measurement replication speed

  concord with previous repilcation start sites

• single pulse and pair pulse

• replication velocity increase with s-phase progression

  this might be correlated with transcription (increase after inhibition)

• mechanism:

  • hyposis:

  • inhibit transcription lead to increased DNA damage

• conclusions

David Weitz: Applications of Single Microbe Sequencing

• background: single microbe sequencing

• Msc RNA-seq(fixed cells)

  • workflow

  • single-cell selection:

  • performance:

  • filter out rRNA using DASH

  • biological question: drug treatment of E coli, time series

  • benchmark

• Microbe-Seq genome sequencing

  • work flow

  • performance

  • single-strain sequencing

  • phages associated with strain

Amos Tanay: Single Cell Models for Deciphering the Birth of Cell-type Specific Epigenetics During Gastrulation

• What is an atlas?

  • dimensional reduction embeddings?
  • gene expression profiles?
  • A group of linked quantitative distributions over all genes !

• how to use atalses in 2020: query projection on atlas

  • for known cell types:

  • for novel cell types:

• how to use atlas in 2022: model dynamics of cells.

Ge Gao: Rationally Design Generative Models for Delineating the Regulator Map in silico

• background: gene expression regulation

• intuition: learn the regulating mechanisms from data