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Genome Biology Studies Look at T-ALL Relapse, Single-Cell Transcriptome Atlas, STARR-seq Software

Researchers in Spain trace relapse-related mutations in adult T cell acute lymphoblastic leukemia (T-ALL). Using whole-genome sequence data on primary and relapsed tumor samples from 19 T-ALL cases, the team analyzed somatic mutation patterns in those tumors and in available genome or exome sequences from more than 200 pediatric or young adults with ALL, retracing tumor drivers and dynamics that lead to relapsed forms of T-ALL. "All results show that, in the T-ALL patients of this cohort, the relapse is driven by genetic mutations that appear in the population of blasts several months before diagnosis, giving rise to a resistant subclone of up to several million cells at the beginning of treatment," the authors report. "Upon treatment … this subclone comes to dominate the T-ALL population at relapse."

A Sun Yat-sen University-led team presents a single-cell transcriptome-based cell atlas spanning more than a dozen adult human tissue types. The researchers did single-cell RNA sequencing on nearly 84,400 individual cells from 15 tissue types and more than 250 cell subtypes, originating from a single adult donor, focusing on gene expression and features in these cells, along with potential cell markers, functional insights, and more. "The resulting high-resolution adult human cell atlas (AHCA) provides a global view of various cell populations and connections in the human body," they write, "and is also a useful resource to investigate the biology of normal human cells and the development of diseases affecting different organs." 

Finally, investigators from the University of Chicago, Yale University, and elsewhere outline an analytical strategy for dealing with high-throughput, targeted sequence data generated with an approach called "self-transcribing active regulatory region sequencing" (STARR-seq) to tally enhancer activity across the genome. The STARRPeaker algorithm "statistically models the basal level of transcription, accounting for potential confounding factors," the team writes, "and accurately identifies reproducible enhancers." In their proof-of-principle analyses on STARR-seq datasets generated on two human cell lines, for example, the authors found that STARRPeaker appeared to compare favorably to other STARR-seq peak processing software such as BasicSTARRseq.