Biology subjects

Cahoon, A.

Publications and source records attributed to Cahoon, A..

4 recordsLinked to original sources

bioRxiv2026

Robust and accessible in situ sequencing with an open-source automated sequencer

Targeted in situ sequencing underlies many spatial transcriptomic, barcoded connectomic, and multiplexed perturbation techniques. Each experiment requires many cycles of chemical reactions and imaging that are time-consuming and error-prone to perform manually, and few commercial platforms can sequence highly diverse RNA barcodes in situ at cellular resolution across large tissue sections. Here we present Seq-o-Matic, an open-source automated in situ sequencer built entirely from commercially available components, which integrates fluidics, heated incubation, and imaging of up to three continuous 18 mm x 50 mm areas on standard microscope slides. Relative to manual sequencing, Seq-o-Matic reduced hands-on time 6- to 17.5-fold and total run time about 2.6-fold. Across three runs spanning two mouse strains and a neonatal marmoset, sequencing quality, measured by signal retention, channel specificity, and phasing, was similar to that of four manually sequenced mouse brains. In four mouse hemispheres sequenced in separate runs (two C57BL/6J, one NOD, and one WSB), consensus cell types were present in every brain and corresponded to those of a manually sequenced C57BL/6J brain. Within cortical excitatory neurons, the two C57BL/6J brains were more similar to each other than were brains of different strains, suggesting that technical variation between runs was small relative to strain differences. Seq-o-Matic thus makes consistent, high-throughput in situ sequencing accessible to laboratories without specialized in situ sequencing expertise.

genomics↗

bioRxiv2025

Map of spiking activity underlying change detection in the mouse visual system

Visual behavior requires coordinated activity across hierarchically organized brain circuits. Understanding this complexity demands datasets that are both large-scale (sampling many areas) and dense (recording many neurons in each area). Here we present a database of spiking activity across the mouse visual system--including thalamus, cortex, and midbrain--while mice perform an image change detection task. Using Neuropixels probes, we record from >75,000 high-quality units in 54 mice, mapping area-, cortical layer-, and cell type-specific coding of sensory and motor information. Modulation by task-engagement increased across the thalamocortical hierarchy but was strongest in the midbrain. Novel images modulated cortical (but not thalamic) responses through delayed recurrent activity. Population decoding and optogenetics identified a critical decision window for change detection and revealed that mice use an adaptation-based rather than image-comparison strategy. This comprehensive resource provides a valuable substrate for understanding sensorimotor computations in neural networks.

neuroscience↗

bioRxiv2023

Connecting single neuron transcriptomes to the projectome in mouse visual cortex

The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neurons role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morphoelectric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.

neuroscience↗

bioRxiv2023

SHIELD: Skull-shaped hemispheric implants enabling large-scale-electrophysiology datasets in the mouse brain

To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. While new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe a novel 3D-printed cranial-replacement implant (SHIELD) enabling electrophysiological recordings from distributed areas of the mouse brain. This skull-shaped implant is designed with customizable insertion holes, allowing dozens of cortical and subcortical structures to be recorded in a single mouse using repeated multi-probe insertions over many days. We demonstrate the procedures high success rate, biocompatibility, lack of adverse effects on behavior, and compatibility with imaging and optogenetics. To showcase the scientific utility of the SHIELD implant, we use multi-probe recordings to reveal novel insights into how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this method enables powerful large-scale electrophysiological measurements for the study of distributed brain computation.

neuroscience↗