4 examples of biological applications for single cell rna-seq · 10/18/2018 · 4 examples of...
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J.J. FOURNIE, Marie TOSOLINI, & Frederic PONT,
Cancer Research Center of ToulouseInstitut Universitaire du Cancer de Toulouse, France
INSA-2018
4 examples of biological applications for single cell RNA-Seq
in cancer & beyond
a Conceptual Revolution in cancer therapy
induce Cancer-Specific Immunity
for all cancers
without antigen
without vaccine
without cancer target
clinical efficacy of new immunotherapies
Adapted from Robert C, NEJM 2014 and Postow MA, NEJM 2015
Survival (%)
What matters for response to cancer immunotherapy ?
No country for old men, Ethan & Joel Coen (2008)
- head or tails ?
Snyder, NEJMed (2014)Alexandrov , Nature (2011)
Tumor mutations matter
F. Pages et al, The Lancet (2018)
Immuno-infiltration of tumor matters
a Conceptual Revolution
Induce Cancer-Specific Immunity
for all cancers without antigen without vaccine without cancer target
1-détecter, identifier & comparer un mélange cellulaire complexe:
l’infiltrat immunitaire des tumeurs
How to extract most infos from small samples ?
1-new methods2- profiling scores3- deep deconvolution
4- single-cell RNA seq
barcoded library
sequence library
• align reads on human genome• quantify each RNA (UMI) for each cell (Barcode)
cell sample:(~500-5,000 cells)
barcoded gel beads(~ 500,000/sample)
• pre-process data (QC per cell, per gene)
• visualize sample by tSNE plot
• analyze data
Single-cell RNA sequencing (10x genomics)
1 file of 10,000 transcriptomes (10,000 transcriptomes of 20,000 genes each)
transform file into sparse matrix
delete artifacts (wrong cells, unexpressed genes), adjust to maxi = 1e4
principal components analysis >>>
clusterize cells
vizualize result: t-SNE maps of all cells
10-15 dimensions 2 dimensions
(qui se ressemble s’assemble)
keep first 10-15 PC (metagenes)
visualizing scRNA-Seq by t-SNE plots
t-SNE plots together the ‘alikes’
t-SNE1
t-SNE2
identifying all blood cells types
t-SNE plot of 12,000 PBMC
PBMC from 3 donors -> 10XGenomics ->: 3’seq by Illumina 3000~85kreads/cell, ~1500 genes/cell ->tSNE plot
Gene expression pattern identifies all cell types
Identifying all cell types in blood
PBMC from 3 donors -> 10XGenomics ->: 3’seq by Illumina 3000~85kreads/cell, ~1500 genes/cell ->tSNE plot
more clusters = more precision
33,000 cellules sanguines d’un autre donneur adulte sain
33,000 single cells from PBMC (Zheng et al, 10X Genomics)
blood cells of a leukemia patient
normal PBMC
Tumors from 2 lung cancer patients
overview de 52,697 single cells issues de 19 biopsies de 5 lung cancer patients(Lambrechts, Nature Medicine, 2018 )
put altogether: 29 tumors from 9 lung cancer patients
a Conceptual Revolution
Induce Cancer-Specific Immunity
for all cancers without antigen without vaccine without cancer target2-retracer une différentiation cellulaire
pseudo-temporal trajectory of a cell fate
young cells
old cells
a Conceptual Revolution
Induce Cancer-Specific Immunity
for all cancers without antigen without vaccine without cancer target
3- trouver la signification d’un transcriptome(d’un ensemble de transcriptomes)
Classic functional analysis of a transcriptome: compare samples, select genes & GSEA
3-Gene Set Enrichment Analysis (GSEA)
1-microarrays
2- genes
functional annotation of t-SNE maps
3: plot signature score of each cell across all t-SNE
1: t-SNE map = 10,000 transcriptomes
2: in each transcriptome: count reads for signature genes
scoring a ‘fake’ signature (random genes) across blood cells
a Conceptual Revolution
Induce Cancer-Specific Immunity
for all cancers without antigen without vaccine without cancer target
4- météorologiques:Prédire l’état prochain d’une cellule
(d’un ensemble de cellules)
the velocity of mRNA
… et leur évolution temporelle
maturation of 18,213 single cells from HippocampusThe velocity of RNA, Kharchenko et al., Nature (2018)
6 take home messages
identify all cell types
all cellular states
any functional annotations
trace back cell lineages
predict cell evolutions
CITE-Seq / Feature -Seq