Hemilineage and birth-order transcription factor codes explain neuronal diversity in Drosophila behavioral circuits
How neurons in behavioral circuits develop
Neurons in Drosophila behavioral circuits diversify according to combinations of transcription factors that establish a hemilineage’s basic structure and sets of transcription factors associated with birth order, a study reports. Published in Nature, the study linked transcriptomes with morphology in selected brain lineages and tested how manipulating some factors affected basic morphology and finer neuronal subtypes. Sexual differentiation was interpreted as adding features to specific subtypes within this framework.
A hemilineage is a group of neurons derived from the same neuroblast and sharing the same Notch signaling activity state. Neurons in a hemilineage generally share major neurite pathways and neurotransmitters, but can differ in fine branching patterns and connection partners. The researchers started with neurons known to regulate male mating behavior and linked transcriptomes to anatomical features in the lineages to which they belong. The analysis focused on postmitotic neurons in selected lineages.
Two transcription factor axes distinguish basic morphology from subtypes
The researchers used genetic lineage labeling to mark descendants of specific neuroblasts, then performed single-cell RNA sequencing (scRNA-seq) at 12 and 48 hours after puparium formation. They also analyzed a condition expressing the apoptosis-inhibiting protein p35 to examine neurons that would normally die. After excluding off-target cells and other cells, the final analysis included 42,844 neurons.
Based on transcription factor expression patterns, the team identified 89 factors that distinguished hemilineages and remained expressed across both developmental time points. Separately, 36 factors were classified as birth-order-related candidates. The molecular markers for hemilineages were compared with histological observations, but the function of every factor was not individually established. The study’s contribution lies in separating the axis that distinguishes different hemilineages from the one that divides subtypes within the same hemilineage.
Some factors were tested by genetic manipulation. In the CREa1B hemilineage, when fd59a or dsf function was eliminated, or TfAP-2 expression was suppressed, neurons still formed and retained gamma-aminobutyric acid (GABA) immunoreactivity, but showed abnormal axon and dendrite morphology. Overexpressing the birth-order-related factors Dan and Pdm3 shifted subtype distributions toward morphologies resembling those of neurons born early and late, respectively. These results support a model in which distinct sets of transcription factors contribute to basic structure and fine subtypes.
The same CREa1B hemilineage included mAL neurons, which are involved in courtship regulation, and mlSEZt neurons, which are involved in feeding regulation. Their behavioral functions were known from earlier studies; this study linked their morphology and molecular markers. Female mAL neurons and mlSEZt neurons in both sexes had the same morphology, but were distinguished by fruitless expression. In transcriptomic analyses that subdivided CREa1B, sex differences were concentrated in the mAL region expressing fruitless. The finding that differences among birth-order-related subtypes were greater than sex differences within the same subtype also supported the interpretation that sexual differentiation modifies features of existing subtypes.
A framework connecting developmental history with circuit structure
By analyzing lineage origin, birth order, and sexual differentiation separately, the study offers a framework for linking molecular identity to circuit morphology. It provides a basis for investigating how neurons that share the same basic pathway can differ in fine morphology and participate in distinct behavioral circuits. However, the morphological changes observed in these manipulation experiments alone do not establish changes in behavior or synaptic function.
The analysis also has limitations. The labeling method may have missed neurons born during embryonic stages, and transcriptomic clusters did not correspond perfectly to anatomical hemilineages. The upstream regulatory processes that generate combinations of transcription factors remain unresolved. The study did not test human neural regeneration therapies or clinical efficacy. The findings are based on the provided public full text of the paper.
Nature, Published online: 27 May 2026. DOI: 10.1038/s41586-026-10526-3
Summary: Resolving the historical operational bottlenecks governing cellular diversity and axonal targeting mechanisms within highly specialized neural circuits, this landmark neuro-genomic study maps the whole-brain connectome trajectory of fruit fly models. By coupling single-cell RNA-sequencing (scRNA-seq) profiles with high-resolution lineage tracing pipelines, the framework decodes the structural orchestration of hierarchical genetic programs driven by precise transcription factor codes. The molecular data tracks how these coordinated cascades programmatically direct multipotent neural stem cells through sequential fate-specification channels, crystallizing distinct functional subdivisions dedicated to processing motivated behaviors such as mating, thereby defining a generalizable computational baseline for translational human neurodevelopmental models.
Researchers studying Drosophila neural circuits could use combinations of hemilineage transcription factors and birth-order-related factors to design experiments that selectively label specific neuronal populations within a lineage. They could select subtypes among neurons sharing a basic pathway, manipulate genes, and compare changes in axon and dendrite morphology and circuit participation. The paper used the intersection of dsf and TfAP-2 to label and manipulate a hemilineage, and combined hemilineage and birth-order-related factors to examine subtypes. The transcriptomic data and analysis objects the researchers report making available in GEO (GSE298900) can also serve as a starting point for selecting candidate factors. Each combination’s selectivity and function still require further validation, and these results alone cannot predict the performance of human cell therapies or neurological disease diagnostics.