Unraveling Tissue Complexity Through Single‐Cell and Spatial Transcriptomics

We are pleased to present this special section of Small Methods, which highlights the rapidly advancing fields of single-cell and spatial transcriptomics. Single-cell transcriptomics and spatial transcriptomics have emerged as transformative tools for high-resolution profiling of gene expression. Single-cell approaches reveal cellular diversity at unprecedented resolution, while spatial transcriptomics preserves the spatial context of gene activity, enabling precise mapping of tissue architecture. Together, these technologies provide complementary insights into biological systems, uncovering cellular heterogeneity, dynamic interactions, and spatially driven molecular processes across diverse fields including developmental biology, cancer biology, immunology, and neuroscience. This collection includes 3 reviews and 10 research articles from prominent scientists with their valuable insights. In this special section, we explore the transformative potential of spatial transcriptomics data analysis. Gao et al. (smtd.2401451) introduce PASSAGE, a deep learning framework for identifying phenotype-associated signatures across heterogeneous spatial slices. Yang et al. (smtd.2400975) propose a novel cell segmentation method UCS, optimized for large-scale subcellular spatial transcriptomics data. Ishaque et al. (smtd.2401123) present Sainsc, a cell-segmentation-free approach for transcriptome-wide, nanoscale-resolution spatial data. Yuan et al. (smtd.2401199) present a kernel-based strategy to model spatially continuous variations of the tissue microenvironment using a new kernel-based strategyFei et al. (smtd.2401056) introduce a membrane-based method that greatly increases the number of genes captured in cells compared to the number captured using nucleus-based methods. These innovations highlight the power of spatially resolved transcriptomics in decoding tissue complexity. Transitioning to integrative analysis of single-cell transcriptomics, spatial transcriptomics data, and other data, this field is reshaping our understanding of tissue architecture and gene expression. Liu et al. (smtd.2401145) developed QR-SIDE, a computational framework that maps spatial heterogeneity and optimizes marker gene contributions for robust deconvolution. Chen et al. (smtd.2401163) developed SpaDA, a spatially aware domain adaptation method integrating transcriptomics, histology, and spatial data to resolve cell-type distributions. These tools exemplify the synergy of multi-modal data in advancing oncology, neuroscience, and precision medicine. Last, Su et al. (smtd.2400991) introduce scPDS, a transformer-based deep learning method that predicts drug sensitivities from scRNA-seq data via pathway activation mapping, bridging transcriptomics, and therapeutic development. The true power of these technologies lies in their applications. Hicks et al. (smtd.2401194) first review the challenges and opportunities of integrating spatially-resolved transcriptomics data across tissues and individuals. Yu et al. (smtd.2401107) review cutting-edge spatial transcriptomics (ST) technologies, computational tools, and their neuroscientific applications. Zeng et al. (smtd.2401171) review the advancements and strategies of spatial omics technologies, summarize their applications in biomedical research, and highlight the power of spatial omics technologies in advancing the understanding of life sciences related to development and disease. Then, Peng et al. (smtd.2401272) developed STExplore, a comprehensive spatial transcriptomics data analysis platform for spatial data integrative analysis. Finally, Ye et al. (smtd.2401192) combine single-cell and spatial transcriptomics to reveal interactions between POSTN+ cancer-associated fibroblasts (CAFs) and CDK16+ tumor cells, driving chemotherapy resistance. These studies exemplify how the integration of these technologies can drive breakthroughs in understanding complex biological systems. Overall, this special section highlights innovative frameworks that integrate single-cell and spatial transcriptomics to map tissue architecture, decode cellular interactions, and uncover disease mechanisms. Advanced machine learning methods, scalable platforms, and multi-omics integration strategies are pushing the boundaries of biomedical research, offering new insights into tumor microenvironments, neural circuits, and immune dynamics. Finally, we extend our heartfelt thanks to the authors, reviewers, and editorial team for their invaluable contributions to this special section. It is our hope that this collection of work will inspire further innovation and collaboration, driving the field forward and unlocking new frontiers in biological research. This study was supported by the STI2030-Major Projects (Grant No. 2022ZD0208000); National Key R&D Program of China (Grant No. 2024YFC3405600); the National Natural Science Foundation of China (Grant No. 82122061); the Fundamental Research Funds for the Central Universities, Xi'an Jiaotong University (Grant No. xtr052024011). The authors declare no conflict of interest. S.S., L.S., and C.Y. wrote the editorial. All authors provided a critical review of the editorial and approved the final editorial before submission. Shiquan Sun, PhD, Professor at the Department of Biostatistics, Xi'an Jiaotong University (XJTU), and Director of the Center for Single-Cell Omics and Health. Before joining XJTU, he completed his postdoctoral training in the Department of Biostatistics at the University of Michigan. He obtained his PhD degree from the Department of Automation at Xi'an Jiaotong University and is trained as both a biostatistician and a computational biologist. He leads a research group primilary interests in developing statistical and computational methods for single-cell and spatial transcriptomics data analysis. Chaoyong Yang, PhD, Professor at the College of Chemistry and Chemical Engineering, Xiamen University, and Joint Professor at the Institute of Molecular Medicine, Shanghai Jiao Tong University. He received his PhD in Analytical Chemistry from the University of Florida and completed his postdoctoral fellowship in bioanalytical chemistry at the University of California, Berkeley. Elected as a Fellow of the Royal Society of Chemistry, he leads a research group pioneering innovative molecular engineering and precision detection technologies, with a focus on Integrated Lab-on-a-Chip Systems, Liquid Biopsies, Single-Cell, and Spatial Omics. Lulu Shang, PhD, tenure-track assistant professor a the Department of Biostatistics at MD Anderson Cancer Center. She received her Ph.D. degree from the Department of Biostatistics at the University of Michigan. Her lab's primary interests include developing effective and efficient statistical and computational methods for single-cell and spatial transcriptomics data analysis, elucidating the functional organization of tissue microenvironment, understanding cellular heterogeneity on tissue, and uncovering mechanisms of disease etiology. Rong Fan, PhD, Professor of Biomedical Engineering at Yale University. He received a B.S. in Applied Chemistry from the University of Science and Technology in China, a Ph.D. in Chemistry from the University of California at Berkeley, and then completed postdoctoral training at the California Institute of Technology, prior to launching his own research laboratory at Yale University in 2010. His current interest is focused on developing microtechnologies for single-cell and spatial omics profiling in order to interrogate functional cellular heterogeneity and inter-cellular signaling networks in human health and disease (e.g., cancer and autoimmunity).