Intraleukemic T cell dynamics in response to Donor Lymphocyte Infusion (DLI)

This website hosts information and data associated with the following publication:

Bachireddy, Azizi, et al.  Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy, Cell Reports, 2021.

Discovery Cohort:

Single-cell (3-prime) transcriptomic data for T cells from samples B1-B46:  

Count matrices for single-cell transcriptomic data for individual samples processed using SEQC.

Combined data matrix from all samples post-normalization and clustering using Biscuit.

Labels for T cells including cell ID, sample ID, patient ID, timing (pre/post-DLI), response, cluster ID, and t-SNE coordinates for all T cells post-filtering and clustering.

Single-cell (5-prime) transcriptomic and TCR data for T cells from samples D1-D7:  

Count matrices for single-cell transcriptomic data processed using Cellranger. 

Validation Cohort:

Single-cell (5-prime) transcriptomic, CITE-seq and TCR data for T cells from samples E1-E3 and E6-E7:  

Count matrices for single-cell transcriptomic and antibody capture processed using Cellranger. 

All raw data can be accessed at the link below

https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002877.v1.p1

Computational Tools:

Symphony: a probabilistic model for integration of single-cell RNA-seq and ATAC-seq data for inferring regulators specific to cell states

https://github.com/dpeerlab/Symphony

Hierarchical Gaussian Process regression model for dynamics of cell states across longitudinal single-cell data:

https://github.com/dpeerlab/dli_gpr