Biology subjects

Jacobs, M. W.

Publications and source records attributed to Jacobs, M. W..

4 recordsLinked to original sources

bioRxiv2025

Structured and Target-Specific Development of Cortico-Cortical Connectivity in the Mouse Visual Cortex

The mammalian cortex exhibits highly stereotyped long-range connectivity, yet the developmental principles that specify precise cortico-cortical projection patterns remain poorly defined. Two dominant models propose that target specificity arises either from early inter-regional exuberant outgrowth followed by pruning, or through initially directed axonal targeting. To resolve this, we systematically mapped the postnatal development of V1 cortico-cortical projection neurons (CCPNs) to eleven higher visual areas (HVAs) in mice using rapid and complementary retrograde, anterograde, and single-cell tracing methods. We found that V1[->]HVA connectivity develops via spatiotemporally staggered axon extension and pruning programs, aligned with target position along the medial-lateral axis. Reciprocal HVA[->]V1 feedback emerges concurrently and is refined over time, yielding gradually aligned bidirectional connectivity. Notably, both multiplexed retrograde tracing and MAPseq-based single-cell profiling revealed that individual V1 neurons initialize and retain specific projection motifs with limited variation over development, arguing against global exuberance followed by selective, inter-areal pruning. Instead, our findings support a directed guidance model, in which distinct V1 CCPN subtypes establish selective projection patterns early, followed by local, target-dependent refinement. This structured yet heterogeneous developmental strategy provides an anatomical framework for how precise long-range cortical networks emerge. HIGHLIGHTSO_LIV1[->]HVA connections form via directed axonal targeting, establishing motifs early with little variation C_LIO_LIMedial targets are innervated earlier and refine gradually, lateral targets later and rapidly C_LIO_LIFeedforward and feedback V1-HVA circuits emerge concurrently C_LIO_LIBidirectional like-to-like V1-HVA connectivity refines across development C_LI

neuroscience↗

bioRxiv2025

Brain-wide connectivity patterns of feedforward and feedback cortico-cortical neurons in the mouse secondary visual cortex

Feedforward and feedback cortico-cortical neurons are distinct yet spatially intermingled subtypes distributed across cortical layers, playing specialized roles in sensory and cognitive processing. However, whether their presynaptic inputs differ to support these functions remains unknown. Using projection-and layer-specific monosynaptic rabies tracing, we mapped brain-wide long-distance inputs to multiple feedforward and feedback neuron types in VISl (also known as LM), the mouse secondary visual cortex. Overall, long-distance input patterns for these feedforward and feedback neurons were largely similar, as all received the majority of their inputs from VISp, the primary visual cortex, along with substantial inputs from various other cortical and visual thalamic regions. Despite their similarities, these feedforward and feedback types differed in the proportion of long-distance cortical inputs originating from specific visual, retrosplenial, and auditory cortices. These findings reveal the input connectivity patterns of cortico-cortical neurons based on feedforward and feedback projections, providing an anatomical framework for future studies on their functions and circuit integration. KEY POINTS1) Feedforward and feedback cortico-cortical projection neurons in VISl form largely distinct populations that are distributed across multiple cortical layers. 2) Four different feedforward and feedback cortico-cortical projection neuron types receive broadly similar patterns of long-distance input across the brain. 3) Despite overall similarities, feedforward and feedback neurons differ in the relative proportions of inputs from specific areas, including visual, retrosplenial, and auditory cortices.

neuroscience↗

bioRxiv2023

Brain-wide Correspondence Between Neuronal Epigenomics and Long-Distance Projections

Single-cell genetic and epigenetic analyses parse the brains billions of neurons into thousands of "cell-type" clusters, each residing in different brain structures. Many of these cell types mediate their unique functions by virtue of targeted long-distance axonal projections to allow interactions between specific cell types. Here we have used Epi-Retro-Seq to link single cell epigenomes and associated cell types to their long-distance projections for 33,034 neurons dissected from 32 different source regions projecting to 24 different targets (225 source [->]target combinations) across the whole mouse brain. We highlight uses of this large data set for interrogating both overarching principles relating projection cell types to their transcriptomic and epigenomic properties and for addressing and developing specific hypotheses about cell types and connections as they relate to genetics. We provide an overall synthesis of the data set with 926 statistical comparisons of the discriminability of neurons projecting to each target for every dissected source region. We integrate this dataset into the larger, annotated BICCN cell type atlas composed of millions of neurons to link projection cell types to consensus clusters. Integration with spatial transcriptomic data further assigns projection-enriched clusters to much smaller source regions than afforded by the original dissections. We exemplify these capabilities by presenting in-depth analyses of neurons with identified projections from the hypothalamus, thalamus, hindbrain, amygdala, and midbrain to provide new insights into the properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription factor binding motifs, and neurotransmitter usage.

neuroscience↗

bioRxiv2022

Bell Jar: A Semi-Automated Registration and Cell Counting Tool for Mouse Neurohistology Analysis

To investigate the anatomical organization of neural circuits across the whole brain, it is essential to register the experimental brain tissues to a reference atlas accurately. This procedure is also a prerequisite to quantify the locations and numbers of cells of interest in specific regions. However, it remains challenging to do registration on experimental tissue due to the intrinsic variation among the specimens, tissue deformation introduced by histological processing, and the potential inconsistency in the judgment of the experimenter during manual annotation. Here, we introduce Bell Jar, a multi-platform analysis tool with semi-automated affine warping of atlas maps onto microscopic images of brain slices and machine learning-based cell detection. Bell Jars intuitive GUI and internal dependency management enable users of all skill levels to obtain accurate results without programming expertise. To compare the performance of Bell Jar with previously published methods1-4, we labeled neurons in the mouse visual cortex with either an engineered rabies virus or an adeno-associated virus (AAV) for neural circuit tracing5, and quantified Bell Jars performance at each step of the pipeline for image alignment, segmentation, and cell counting. We demonstrated that Bell Jars output is as reliable as manual counting by an expert; it is more accurate than currently available techniques even with noisy data and takes less time with fewer user interventions. Bell Jar is an easy-to-navigate application that provides a reproducible, automated analysis workflow to facilitate the precise mapping of histological images of the mouse brain to the reference atlas and the quantification of cellular signals it is trained to recognize.

neuroscience↗