Katsouri, L. | bioRxiv Science

bioRxiv2026

High-throughput serial two-photon tomography with BakingTray and BrainSaw

Volumetric imaging is increasingly important in fields such as neuroscience, where quantifying structure and mapping features are inherently three-dimensional problems. Block-face serial two-photon tomography (STPT) has emerged as a powerful approach for high-resolution, whole-organ volumetric imaging. STPT avoids complex sample preparation and balances image quality, speed, and scalability. However, STPT remains niche, in part because of the absence of flexible, general-purpose control software. Here we present BakingTray, an open-source software platform for STPT that extends ScanImage. BakingTrays modular framework accommodates diverse hardware configurations and allows users to build systems customised to their needs. Twelve such systems, which we call BrainSaws, have been constructed worldwide since 2016, with more under construction, and have so far contributed to 35 research papers. The three systems at the Sainsbury Wellcome Centre alone have acquired over 7,000 samples in a busy multi-user facility. We demonstrate the platform across its core neuroscience applications: electrode track localisation, brain-wide cell counting, and bulk projection mapping. Beyond these, we map the cell-type-specific synaptic output of inhibitory neurons in the ventral lateral geniculate nucleus and, with whole-mount staining, amyloid plaques brain-wide in a mouse model of Alzheimers disease. BakingTray is a mature platform for groups or facilities needing a customised volumetric imaging solution, whether built new or converted cheaply from an existing two-photon microscope.

neuroscience↗

bioRxiv2025

Dissecting novel object exploration: a fully automated homecage-based novel object recognition test

The novel object recognition test is a frequently used memory test in rodents. Due to its ethological nature, cross-species relevance, and specificity to testing hippocampal and parahippocampal function, it has been widely applied in basic and translational research. However, its implementation proves challenging due to multiple uncontrolled factors. Here, we describe a fully automated homecage-based novel object recognition test for assessing long-term object memory in mice. We present an empirically guided computational model to show the robustness of this approach despite ambiguity in defining exploratory behaviours. We show that mice preferentially explored novel compared to familiar objects after 24-hour and 7-day retention periods, starting to discern them while still a distance away. The findings were replicated across two facilities. Furthermore, the ability to recognise the novel object depends on the mouses prior interactions with the replaced object after 24 hours, but not after 7 days. Finally, we showed that external factors may introduce undesired exploration biases, which can be addressed using relative instead of absolute discrimination measures. The fully automated homecage-based object recognition test will improve standardisation, rigour, and reproducibility, as well as expand our understanding of the factors influencing object exploratory behaviours and object memory. MotivationRecognition of objects as novel or familiar is an important cognitive memory function with cross-species relevance. Extensive work has provided a good understanding of the brain regions involved. Despite the apparent simplicity of novel object recognition (NOR) tests, they remain challenging due to their sensitivity to various uncontrolled experimental factors and differences in study design. Here, we report a fully automated standardised NOR test carried out in a mouses homecage, which minimises previously reported variability in NOR tests. HighlightsO_LIFully automated novel object recognition test for assessing long-term object memory in the mouses homecage C_LIO_LIA robust analysis pipeline described C_LIO_LITest replicated in two facilities with comparable results C_LIO_LIAn empirically guided computational model pointing to the robustness of this approach introduced C_LIO_LIMajor factors affecting the ability to discriminate novel from familiar objects, including object exploration bias, are described C_LI

neuroscience↗