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| Table of Contents |
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| Spatial omics illuminates tumor heterogeneity |
| Neha Srinivas, Serap Erdogmus, Gurkan Mollaoglu |
| Emerging spatial omics, in vivo CRISPR, and lineage-tracing technologies are transforming the study of intratumoral heterogeneity. Srinivas et al. review how integrating spatial proteomics, transcriptomics, functional genomics, and clonal tracing is uncovering the cellular interactions in heterogeneous tumor ecosystems that drive tumor progression, evolution, and treatment response. |
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| Induced pluripotent stem cell-derived macrophages enable broad modeling of human inflammasome signaling |
| Chloe M. McKee, Melanie Cranston, Emma C. McKay, Mohammad Arefian, Thea J. Mawhinney, Ben C. Collins, Rebecca C. Coll |
| Current in vitro macrophage models have limitations for studying inflammasome function. McKee et al. demonstrate that iPSC-derived macrophages are broadly comparable to monocyte-derived macrophages in response to a wide range of inflammasome priming and activation stimuli and thus hold promise as a physiologically relevant model for human inflammasome studies. |
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| A DirectView focus reduction assay for high-throughput quantification of neutralizing antibodies against influenza A and B viruses |
| Chenchen Feng, Thomas Rowe, Michael Currier, Ryan Dong, Ying Huang, Ginger Atteberry, Li Wang, Masato Hatta, C. Todd Davis, David E. Wentworth, Bin Zhou |
| Feng et al. establish DirectView FRA, a high-throughput focus reduction assay that rapidly quantifies influenza-neutralizing antibodies by visualizing viral foci using a fluorogenic neuraminidase substrate. The assay eliminates immunostaining while maintaining accuracy comparable to traditional FRA, enabling large-scale serological analysis for influenza surveillance during epidemics and pandemics. |
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| Rapid discovery of cell-surface glycosylation regulators using a lectin-based magnetic CRISPR screen |
| Jimmy Kim, Halen Kovacs, Simon Wisnovsky |
| Kim et al. present a lectin-based magnetic CRISPR screening platform (Lec-MACS) for identifying regulators of cell-surface glycosylation. Lec-MACS matches FACS performance while offering greater speed and scalability for adherent cell lines. A genome-wide Lec-MACS screen also reveals an unexpected link between DNA damage signaling and cancer cell sialylation. |
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| IAN, an intelligent system for omics data analysis and discovery |
| Vijayaraj Nagarajan, Reiko Horai, Guangpu Shi, Cheng-Rong Yu, Jaanam Gopalakrishnan, Manoj Kumar Yadav, Michael H. Liew, Calla Gentilucci, Rachel R. Caspi |
| Nagarajan et al. present IAN, an R-based multi-agent LLM system that enables interpretation of systems-level gene expression through enrichment and network data. They demonstrate the reliability of IAN through input groundedness and semantic evaluations, as well as consistent performance across LLM backends. |
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| Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies |
| Jiadong Mao, Jarny Choi, Kim-Anh Lê Cao |
| Mao et al. present Φ-Space ST, a platform-agnostic method for annotating continuous cell states in spatial transcriptomics using multiple scRNA-seq references. Validated across Visium, CosMx, Xenium, and Stereo-seq cancer datasets, Φ-Space ST reveals niche-specific cell-type co-presence patterns and clonal heterogeneity without requiring cell segmentation. |
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| DeepFaceMouse enables scalable prediction of large-scale brain activity from facial dynamics in mice |
| Kemal Ozdemirli, Tenesha Connor, Kaleb Kim, Ersan Alp Unlu, Macit Emre Lacin, Miguel Maldonado, Frederick Bell, Thiago Peixoto Leal, Caglar Oksuz, Anthony Sloan, Nicholas Sarn, Anthony Chomyk, Bruce Trapp, Justin D. Lathia, Ignacio Mata, Charis Eng, Murat Yildirim |
| Ozdemirli et al. introduce DeepFaceMouse, a deep learning framework that decodes cortex-wide brain activity from facial dynamics in mice. The method achieves high precision and robust generalization across behaviors and disease models, enabling scalable mapping of brain-behavior relationships. |
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| A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation |
| Patrick Jendritza, Rickard Liljemalm, Thomas Stieglitz, Pascal Fries, Christopher Murphy Lewis |
| Jendritza et al. introduce a μECoG that enables high-resolution functional mapping of brain regions in rats, cats, and marmosets. The μECoG can be repeatedly penetrated with intracortical arrays for dense recording of functional targets and is transparent, enabling optogenetic manipulations. The μECoG facilitates multi-area laminar recordings to investigate cortico-cortical communication. |
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| Direct quantification of the metabolic heat output of individual Drosophila brains |
| Kanishka Panda, Rohith Mittapally, Qianqian Chen, Akshay Manoj Bhaskaran, Pramod Reddy, Edgar Meyhofer, Swathi Yadlapalli |
| Panda et al. develop a nanowatt-resolution calorimetric platform for quantifying the metabolic heat output of Drosophila brains and reproductive tissues. They demonstrate sex- and genotype-dependent metabolic patterns, including higher metabolic output of young female brains compared to males, and reduced metabolic output in parkin mutant fly brains. |
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| Unsupervised deep learning enables blur-free resolution enhancement in two-photon microscopy |
| Haruhiko Morita, Shuto Hayashi, Takahiro Tsuji, Daisuke Kato, Hiroaki Wake, Teppei Shimamura |
| Two-photon microscopy reveals deep-tissue biology but produces blurry, hard-to-analyze 3D images. Morita et al. develop TENET, a physics-informed unsupervised deep learning framework that deblurs, enhances resolution, and segments microscopy volumes without requiring ground-truth data. Applied to live brain tumor imaging, TENET reveals dynamic microglia-tumor interactions invisible to conventional approaches. |
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| Precision tumor-on-chip for personalized assessment of drug efficacy and immune cell delivery |
| Elena Kremneva, Johannes Smolander, Mimosa Peltokangas, Mikaela Grönholm, Lilja Lahtinen, Shadi Jansouz, Sanna Vainionpää, Bassel Alsaed, Kristian Borenius, Anniina Palojärvi, Pauliina Junttila, Jere Kettunen, Tuomas Pylkkö, Michaela Feodoroff, Gabriella Antignani, Harri K. Mustonen, Markus Vähä-Koskela, Tuan Nguyen, Prateek Singh, Toni T. Seppälä, Eva Sutinen, Reetta Riikonen, Satu Juhila, Vincenzo Cerullo, Hanna Seppänen, Ilkka Ilonen, Sebastien Mosser, Heidi M. Haikala |
| Kremneva et al. present a microfluidic tumor-on-chip platform integrating patient-derived organoids with vascular flow to simulate physiological drug delivery. This model captures treatment resistance features at single-cell resolution and models immune cell migration. By linking transport dynamics with therapeutic efficacy, the platform provides a scalable tool for personalized drug evaluation. |
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| Simple 3D-printed stirred bioreactor enhances retinal organoid production via improved oxygenation |
| Kyle H. Schwab, Philsang Hwang, Ki Yoon Nam, Zachary Batz, Suja Hiriyanna, Florian Régent, Nicole Y. Morgan, Peter I. Lelkes, Tiansen Li |
| Schwab et al. identify oxygen depletion during early retinal organoid differentiation as an overlooked source of poor yield and degeneration. They develop a simple 3D-printed stirred bioreactor that restores physiological oxygenation and improves retinal organoid yield, size, viability, and reproducibility. |
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| OrgLine: A versatile pipeline for organoid morphometry using detector-guided prompts |
| Xun Deng, Xinyu Hao, Thomas Herget, Mei Gao, Mathias Winkel, Feng Tan, Lun Hu, Pengwei Hu |
| Deng et al. present OrgLine, a bright-field organoid analysis pipeline that combines pre-trained detection with prompt-guided segmentation. Using a curated benchmark, they evaluate transfer across datasets and extend the framework to morphology analysis, time-series tracking, and passaging support. |
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| Optimizing microhomology-based genome editing by engineering DNA polymerase θ for improved efficiency and reduced on-target indels |
| Wanyi Wang, Meng Zhou, Tianshan Ji, Qin Jiang, Jinyi Zhao, Zimeng Xu, Ruofei Liu, Yining Luo, Feng Han, Le Cong, Lei Fan, Chen Chen, Chengkun Wang |
| Wang et al. present engineered polymerase θ functional domains that enable efficient and precise microhomology-mediated genome editing. The resulting pPET and hPET systems outperform existing strategies, support kilobase-scale knock-in, and retain high activity in cell-cycle-restricted states, providing a versatile alternative to HDR for precise genome engineering. |
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| A computational method to design broad-spectrum T cell-inducing vaccines applied to Betacoronaviruses |
| Phil Palmer, Sofiya Fedosyuk, Srivatsan Parthasarathy, Jonathan Holbrook, Charlotte George, Laura O’Reilly, Lara Wiegand, George William Carnell, Jonathan Luke Heeney, Sneha Vishwanath |
| Palmer et al. present Spectravax, a computational method for designing broad-spectrum T cell vaccine antigens that accounts for genetic diversity in both host and pathogen populations. Applied to Betacoronaviruses, Spectravax elicited cross-reactive T cell immunity against SARS-CoV, SARS-CoV-2, and MERS-CoV in mice. |
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| A high-throughput, end-to-end pipeline for extracellular miRNA biomarker discovery from human biofluids |
| Abbas Hakim, Jennifer N. Chousal, Srimeenakshi Srinivasan, Anelizze Castro-Martinez, Basant ElGhayati, Marina Mochizuki, Tyler Ostrander, Cassandra Wauer, Peter De Hoff, Priyadarshini Pantham, Louise C. Laurent |
| Hakim and Chousal et al. develop an end-to-end workflow for extracellular RNA analysis, including an automated extraction method and data normalization, enabling consistent and scalable analysis of circulating RNA biomarkers. They show that plasma provides the most reliable signal of the tested biofluids, supporting standardized approaches to biomarker discovery. |
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| Uncertainty-aware graph structure optimization with ensemble learning for enhanced cancer gene identification |
| Zihan Hu, Xiangzheng Fu, Ruyi Zheng, Yangyuan Chen, Tao Wang, Linlin Zhuo, Yiting Ke, Zhen Li, Quan Zou |
| Hu et al. develop NexusGene, a method that integrates uncertainty-aware graph structure learning with clustering-enhanced ensemble learning to improve cancer driver gene identification. By refining noisy biological networks and modeling node-level uncertainty, NexusGene achieves robust predictions across diverse pan-cancer and cancer-specific datasets. |
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| Systematic benchmarking of CUT&Tag improves the reliability and reproducibility of chromatin analysis |
| Josiah D. Murray, Atrayee Ray, Khanmi Kasomva, Erica Steen, Cary Stelloh, Kirthi Pulakanti, Phillip A. Doerfler, Navonil De Sarkar, Qiongzi Qiu, Yong Liu, Aron M. Geurts, Allen W. Cowley Jr., Alison E. Meyer, Mingyu Liang, Sridhar Rao |
| Murray et al. identify key experimental factors that strongly influence CUT&Tag performance across chromatin targets. By optimizing cell input, peak calling, duplicate handling, and crosslinking conditions, they provide practical guidance to improve the accuracy and reproducibility of chromatin profiling experiments across diverse biological systems. |
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