@article {70293, title = {Machine learning-guided spatial omics for tissue-scale discovery of cell-type-specific architectures}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 13}, abstract = {

Multiplexed protein imaging enables spatially resolved analysis of molecular organization in tissues, but existing spatial proteomics platforms remain constrained in scalability, throughput, and integration with RNA measurements and interpretable computational analysis. Here, we present an integrated spatial omics framework that combines highly multiplexed protein and RNA imaging with explainable machine learning to map cell-type-specific molecular and structural architectures at tissue scale. Using this platform, we simultaneously profiled up to 46 proteins and 79 RNA species across \~370,000 cells in intact mouse brain tissue at diffraction-limited subcellular resolution (\~260 nm). We developed a scalable, open-source computational pipeline for large-scale image processing and analysis, and show that nuclear protein and chromatin features alone are sufficient to accurately classify brain cell types and their spatial organization. Incorporation of explainable deep learning further enabled identification of human-interpretable, cell-type-specific subnuclear structural features directly from imaging data, with independent quantitative validation. Together, this integrated experimental and computational framework enables tissue-scale spatial proteomics-based cell-type classification and structural feature discovery, providing a broadly applicable platform for mechanistic studies, high-content screening, and translational applications.

}, doi = {10.64898/2026.02.12.705598}, url = {https://www.biorxiv.org/content/early/2026/02/13/2026.02.12.705598}, author = {Lian, Yumin and Adjavon, Diane-Yayra and Kawase, Takashi and Kim, Jun and Fleishman, Greg and Preibisch, Stephan and Funke, Jan and Liu, Zhe J.} } @article {69457, title = {Deep-tissue transcriptomics and subcellular imaging at high spatial resolution}, journal = {Science}, year = {2025}, month = {2025 Feb 20}, abstract = {

Limited color channels in fluorescence microscopy have long constrained spatial analysis in biological specimens. Here, we introduce cycle Hybridization Chain Reaction (HCR), a method that integrates multicycle DNA barcoding with HCR to overcome this limitation. cycleHCR enables highly multiplexed imaging of RNA and proteins using a unified barcode system. Whole-embryo transcriptomics imaging achieved precise three-dimensional gene expression and cell fate mapping across a specimen depth of ~310 μm. When combined with expansion microscopy, cycleHCR revealed an intricate network of 10 subcellular structures in mouse embryonic fibroblasts. In mouse hippocampal slices, multiplex RNA and protein imaging uncovered complex gene expression gradients and cell-type-specific nuclear structural variations. cycleHCR provides a quantitative framework for elucidating spatial regulation in deep tissue contexts for research and potentially diagnostic applications.

bioRxiv preprint:\ 10.1101/2024.05.17.594641

}, doi = { 10.1126/science.adq2084}, url = {https://www.science.org/doi/10.1126/science.adq2084}, author = {Gandin, Valentina and Kim, Jun and Yang, Liang-Zhong and Lian, Yumin and Kawase, Takashi and Hu, Amy and Rokicki, Konrad and Fleishman, Greg and Tillberg, Paul and Aguilera Castrejon, Alejandro and Stringer, Carsen and Preibisch, Stephan and Liu, Zhe J.} } @article {69825, title = {HortaCloud: An Open and Collaborative Platform for Whole Brain Neuronal Reconstructions}, journal = {bioRxiv}, year = {2025}, month = {2025 Mar 13}, abstract = {

HortaCloud is a cloud-based, open-source platform designed to facilitate the collaborative reconstruction of long-range projection neurons from whole-brain light microscopy data. By providing virtual environments directly within the cloud, it eliminates the need for costly and time-consuming data downloads, allowing researchers to work efficiently with terabyte- scale volumetric datasets. Standardization of computational resources in the cloud make deployment easier and more predictable. The pay-as-you-go cloud model reduces adoption barriers by eliminating upfront investments in expensive hardware. Finally, HortaCloud{\textquoteright}s decentralized architecture enables global collaboration between researchers and between institutions.

}, doi = {10.1101/2025.03.13.642887}, url = {https://www.biorxiv.org/content/early/2025/03/14/2025.03.13.642887}, author = {Rokicki, Konrad and Schauder, David and Olbris, Donald J. and Goina, Cristian and Clements, Jody and Edson, Patrick and Kawase, Takashi and Svirskas, Robert and Arshadi, Cameron and Feng, David and Chandrashekar, Jayaram and Ferreira, Tiago A. and MouseLight Project Team} } @article {69548, title = {Cohesin prevents cross-domain gene coactivation.}, journal = {Nat Genet}, year = {2024}, month = {2024 Jul 24}, abstract = {

The contrast between the disruption of genome topology after cohesin loss and the lack of downstream gene expression changes instigates intense debates regarding the structure-function relationship between genome and gene regulation. Here, by analyzing transcriptome and chromatin accessibility at the single-cell level, we discover that, instead of dictating population-wide gene expression levels, cohesin supplies a general function to neutralize stochastic coexpression tendencies of cis-linked genes in single cells. Notably, cohesin loss induces widespread gene coactivation and chromatin co-opening tens of million bases apart in cis. Spatial genome and protein imaging reveals that cohesin prevents gene co-bursting along the chromosome and blocks spatial mixing of transcriptional hubs. Single-molecule imaging shows that cohesin confines the exploration of diverse enhancer and core promoter binding transcriptional regulators. Together, these results support that cohesin arranges nuclear topology to control gene coexpression in single cells.

}, issn = {1546-1718}, doi = {10.1038/s41588-024-01852-1}, url = {https://doi.org/10.1038/s41588-024-01852-1}, author = {Dong, Peng and Zhang, Shu and Gandin, Valentina and Xie, Liangqi and Wang, Lihua and Lemire, Andrew L and Li, Wenhong and Otsuna, Hideo and Kawase, Takashi and Lander, Arthur D and Chang, Howard Y and Liu, Zhe J} } @article {68507, title = {A connectome and analysis of the adult Drosophila central brain.}, journal = {Elife}, volume = {9}, year = {2020}, month = {2020 Sep 07}, abstract = {

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly{\textquoteright}s brain.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, Female, Male, Neurons, Synapses}, issn = {2050-084X}, doi = {10.7554/eLife.57443}, author = {Scheffer, Louis K and Xu, C Shan and Januszewski, Michal and Lu, Zhiyuan and Takemura, Shin-ya and Hayworth, Kenneth J and Huang, Gary B and Shinomiya, Kazunori and Maitlin-Shepard, Jeremy and Berg, Stuart and Clements, Jody and Hubbard, Philip M and Katz, William T and Umayam, Lowell and Zhao, Ting and Ackerman, David and Blakely, Tim and Bogovic, John and Dolafi, Tom and Kainmueller, Dagmar and Kawase, Takashi and Khairy, Khaled A and Leavitt, Laramie and Li, Peter H and Lindsey, Larry and Neubarth, Nicole and Olbris, Donald J and Otsuna, Hideo and Trautman, Eric T and Ito, Masayoshi and Bates, Alexander S and Goldammer, Jens and Wolff, Tanya and Svirskas, Robert and Schlegel, Philipp and Neace, Erika and Knecht, Christopher J and Alvarado, Chelsea X and Bailey, Dennis A and Ballinger, Samantha and Borycz, Jolanta A and Canino, Brandon S and Cheatham, Natasha and Cook, Michael and Dreher, Marisa and Duclos, Octave and Eubanks, Bryon and Fairbanks, Kelli and Finley, Samantha and Forknall, Nora and Francis, Audrey and Hopkins, Gary Patrick and Joyce, Emily M and Kim, SungJin and Kirk, Nicole A and Kovalyak, Julie and Lauchie, Shirley A and Lohff, Alanna and Maldonado, Charli and Manley, Emily A and McLin, Sari and Mooney, Caroline and Ndama, Miatta and Ogundeyi, Omotara and Okeoma, Nneoma and Ordish, Christopher and Padilla, Nicholas and Patrick, Christopher M and Paterson, Tyler and Phillips, Elliott E and Phillips, Emily M and Rampally, Neha and Ribeiro, Caitlin and Robertson, Madelaine K and Rymer, Jon Thomson and Ryan, Sean M and Sammons, Megan and Scott, Anne K and Scott, Ashley L and Shinomiya, Aya and Smith, Claire and Smith, Kelsey and Smith, Natalie L and Sobeski, Margaret A and Suleiman, Alia and Swift, Jackie and Takemura, Satoko and Talebi, Iris and Tarnogorska, Dorota and Tenshaw, Emily and Tokhi, Temour and Walsh, John J and Yang, Tansy and Horne, Jane Anne and Li, Feng and Parekh, Ruchi and Rivlin, Patricia K and Jayaraman, Vivek and Costa, Marta and Jefferis, Gregory Sxe and Ito, Kei and Saalfeld, Stephan and George, Reed and Meinertzhagen, Ian A and Rubin, Gerald M and Hess, Harald F and Jain, Viren and Plaza, Stephen M} } @article {65382, title = {Conservation and divergence of related neuronal lineages in the central brain.}, journal = {eLife}, volume = {9}, year = {2020}, month = {2020 Apr 07}, abstract = {

Wiring a complex brain requires many neurons with intricate cell specificity, generated by a limited number of neural stem cells. central brain lineages are a predetermined series of neurons, born in a specific order. To understand how lineage identity translates to neuron morphology, we mapped 18 central brain lineages. While we found large aggregate differences between lineages, we also discovered shared patterns of morphological diversification. Lineage identity plus Notch-mediated sister fate govern primary neuron trajectories, whereas temporal fate diversifies terminal elaborations. Further, morphological neuron types may arise repeatedly, interspersed with other types. Despite the complexity, related lineages produce similar neuron types in comparable temporal patterns. Different stem cells even yield two identical series of dopaminergic neuron types, but with unrelated sister neurons. Together, these phenomena suggest that straightforward rules drive incredible neuronal complexity, and that large changes in morphology can result from relatively simple fating mechanisms.

}, issn = {2050-084X}, doi = {10.7554/eLife.53518}, author = {Lee, Ying-Jou and Yang, Ching-Po and Miyares, Rosa L and Huang, Yu-Fen and He, Yisheng and Ren, Qingzhong and Chen, Hui-Min and Kawase, Takashi and Ito, Masayoshi and Otsuna, Hideo and Sugino, Ken and Aso, Yoshi and Ito, Kei and Lee, Tzumin} } @article {49236, title = {Color depth MIP mask search: a new tool to expedite Split-GAL4 creation.}, journal = {bioRxiv}, year = {2018}, month = {2018 May 09}, abstract = {

The GAL4-UAS system has proven its versatility in studying the function and expression patterns of neurons the Drosophila central nervous system. Although the GAL4 system has been used for 25 years, recent genetic intersectional tools have enabled genetic targeting of very small numbers of neurons aiding in the understanding of their function. This split-GAL4 system is extremely powerful for studying neuronal morphology and the neural basis of animal behavior. However, choosing lines to intersect that have overlapping patterns restricted to one to a few neurons has been cumbersome. This challenge is now growing as the collections of GAL4 driver lines has increased. Here we present a new method and software plug-in for Fiji to dramatically improve the speed of querying large databases of potential lines to intersect and aid in the split-GAL4 creation. We also provide pre-computed datasets for the Janelia GAL4 (5,738 lines) and VT GAL4 (7,429 lines) of the Drosophila central nervous system (CNS). The tool reduced our split-GAL4 creation effort dramatically.

}, doi = {10.1101/318006}, author = {Otsuna, Hideo and Ito, Masayoshi and Kawase, Takashi} }