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