Blinatumomab Maintenance After Allogeneic Hematopoietic Cell Transplantation for B-Lineage Acute Lymphoblastic Leukemia

Authors

Yuanxin Wang
Merve Dede
Vakul Mohanty
Jinzhuang Dou
Ziyi Li
Ken Chen

Publication Date

12-16-2024

Journal

Cell Reports Methods

Abstract

Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, enhancing our ability to identify therapeutic targets for disease interventions.

Keywords

Single-Cell Analysis, Humans, Animals, RNA-Seq, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Gene Regulatory Networks, Transcriptome, Single-Cell Gene Expression Analysis, cell transitions, gene regulatory network, dynamic systems, cell differentiation, cell development, carcinogenesis, single-cell RNA sequencing, differential equations, gene expression correlation, statistical analysis

DOI

10.1016/j.crmeth.2024.100913

PMID

39644902

PMCID

PMC11704623

PubMedCentral® Posted Date

12-6-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes