This website hosts the datasets, computational tools, and supplementary materials linked to the following publication:
​Maurer K*, Park CY*, Mani S, Borji M, Raths F, Gouin III KH, Penter L, Jin Y, Zhang JY, Shin C, Brenner JR, Southard J, Krishna S, Lu W, Lyu H, Abbondanza D, Mangum C, Olsen LR, Neuberg DS, Bachireddy P, Glezer EN, Farhi SL, Li S, Livak KL, Ritz J, Coiffeur RJ, Wu CJ^, Azizi E^.​ Coordinated immune networks in leukemia bone marrow microenvironments distinguish response to cellular therapy, Science Immunology. 2025. DOI: 10.1126/sciimmunol.adr0782

Code and Data Access

• Python Notebooks:
  Notebooks for generating the figures presented in the publication are available on GitHub:
  github.com/azizilab/dli_reproducibility
• Raw Data:
  Deposited on dbGaP with accession ID: phs003630.v1.p1
• Processed Data Files:
  • Single-cell RNA-seq data is available on GEO with accession ID: GSE255530 and metadata is available here
  • Single-cell TCR-seq data is available here
  • CODEX spatial protein data can be accessed on the Google Cloud bucket:
    CODEX data
  • Singular Genomics spatial transcriptomic and protein data is available here:
    Singular Genomics data

Computational Tools

• DIISCO (Dynamic Intercellular Interaction Inference):
  DIISCO is a computational framework designed to infer dynamic intercellular interactions using longitudinal data.
  • Read more about DIISCO in the related publication below and our Software page
    • Park C*, Mani S*, Beltran-Velez N, Maurer K, Gohil S, Li S, Huang T, Knowles DA, Wu CJ, Azizi E. A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data. Genome Research 2024 (featured on the cover, highlights, code).
  • Access the tool on GitHub:
    github.com/azizilab/DIISCO_public