Single Cell SequencingA Ari Hakimi MD

Dept of Surgery, Urology

Immunogenomics and Precision Oncology Platform

MSKCC

Differences within cell type Differentiation trajectories- cell type vs cell state

High-resolution heterogeneity

Integrative analysis- TCR-seq, ATAC-seq, whole-genome

Spatial sequencing is of major interest

Why single-cell RNA-seq?

Types of single cell sequencing

Stuart et al Nat Rev Gen 2019

Stuart et al Nat Rev Gen 2019

Cell of origin studies

Matthew D. Young et al. Science 2018;361:594-599

Copyright © 2018, American Association for the Advancement of Science

Matthew D. Young et al. Science 2018;361:594-599

Copyright © 2018, American Association for the Advancement of Science

Importance of Batch Correction

• Merged Mouse samples• P1: Parental replicate 1

• P2: Parental replicate 2

• P3: Parental replicate 3

• Q1: Experimental replicate 1

T cell cluster enrichmentBefore Batch Correction

After Batch Correction

No batch correction (N = 24):

color by region

No batch correction (N = 24):

color by patient

Batch correct each patient (N = 18):

color by region

Batch correct each patient (N = 18):

color by patient

Batch correct each region (N = 18):

color by region

Batch correct each region (N = 18):

color by patient

Increasing strictness of correction

Once the entire dataset is in- need to quantify this effect more rigorously

Batch correction – human data

UT1: N = 26177 UT2: N = 13213

t1 (Nivo-exposed):

N = 30547

t2 (Ipi/Nivo-resistant):

N = 30547

t3 (Ipi/Nivo-mixed response):

N = 38660

t4 (Ipi/Nivo-complete response):

N = 38660

N = 167283

Following batch correction using mutual nearest

neighbors (MNNCorrect)

Six patients w/ site-matched

scRNA+TCR-seq: 2 untreated (UT1-2),

4 treated (t1-4)

+ exp design schematic, clinical characteristics, any Sounak path data

4 tissue types

Tumor: N = 107806 Lymph Node: N = 3835 Normal Kidney: N = 24096 PBMC: N = 31546

CD45 CA9

CD3D CD14 CD79A

Distribution of immune + malignant cell types

CA9

Cluster 0- Hypoxia/Metastasis

P = 0.003 P = 0.009

Cluster 4- Type 1 Interferon Signaling

P = 0.0 P = 0.0

Multiple CA9 (tumor specific) phenotypes – correlate with unique TMEs

P = 0.0

Cluster 7- Antigen Processing/Presentation + myeloid/CD4 T cell infiltrated

P = 0.0 P = 0.0 P = 0.0 P = 0.0

T/B/NK cell cluster prevalence by tissue

Tumor Normal Kidney PBMC

Myeloid cluster prevalence by tissue

Tumor Normal Kidney PBMC

UT1 UT2 t1

t2 t3 t4

T/B/NK cell cluster prevalence by tumor region

Capturing cell transitional states

• New methods to capture the relationship between tissues + continuity of gene expression underlying immune cell differentiation/development

• PCA: linear embedding- useful for visualization- not useful for capturing underlying structure/differentiation

• Diffusion maps: non-linear embedding that emphasize transitions in the data- typically used when processes are continuous (i.e. T cells, monocytes from blood → tumor)

Diffusion Maps: Non-linear embeddings for scRNA-seq data

UT1 T cells

Associations with response in BMS009

C2: NK cells C23: B cells

C0: CD8+ T cells C10: FOXP3+ Tregs

Binary classification using RECIST criteria: responders defined as CR/PR/S; non-responders as PD

Conclusions

• Single cell sequencing continues to evolve

•Batch correction critical when merging data sets/types• Easy to find spurious associations

• Transition between cell states can be capture with new embedding techniques (ie diffusion maps)

•Unique opportunities to study the TME and TME evolution on treatment