Susanne Reinhardt

The paper compares regulatory changes in larval and adult zebrafish spinal progenitors after injury, showing they converge to a lesion-reactive state with distinct gene expression patterns.

This study uses a cross-species bone marrow niche model to show that plasticity in the murine niche supports human HSC engraftment, highlighting its role in stem cell function.

The paper compares the roles of canonical and non-canonical PRC1 sub-complexes in neural stem cell regulation during neocortex development, finding canonical PRC1 has a more significant impact.

This paper introduces SUM-PAINT, a high-throughput imaging method for single-protein spatial resolution, and discovers a new synapse type through extensive multiprotein analysis.

The paper investigates cell lineage relationships in zebrafish fin regeneration using single-cell RNA sequencing and lineage tracing, revealing compartmentalized osteoblast and blastema contributions to bone formation.

The paper uses fate-mapping, single-cell RNA-sequencing, and computational methods to show that major hematopoietic lineages diverge within HSCs, identifying a self-renewing HSC subpopulation and a direct thrombopoiesis pathway.

This study shows that loss of SETD1B in oocytes causes redistribution of H3K4me3, with loss at active promoters and gain at CpG-rich regions, revealing distinct roles for SETD1B and MLL2 in epigenetic regulation.

This study maps β-cell regeneration in zebrafish using single-cell transcriptomics, identifying hybrid β/δ cells as key contributors to diabetes recovery.

The paper identifies high ROS levels in quiescent adult hippocampal stem cells and shows how physical activity triggers their activation through a Nox2-dependent ROS surge.

The paper uses single-cell transcriptomics to reveal heterogeneity in zebrafish thyroid thyrocytes and characterizes surrounding cell types.

The paper uses single-cell transcriptomics to analyze how Amyloid-beta42 and Interleukin-4 affect neural stem/progenitor cell plasticity in adult zebrafish brain, identifying gene expression changes and cell-cell communication alterations.

This paper introduces GERAS, a machine learning framework that classifies single-cell transcriptomes into chronological stages with high accuracy and identifies aging-related factors in zebrafish and human pancreatic cells.