The emerging landscape of spatial profiling technologies

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Metric multidimensional scaling for large single-cell datasets using neural networks

Metric multidimensional scaling is one of the classical methods for embedding data into low-dimensional Euclidean space. It creates the low-dimensional embedding by approximately preserving the pairwise distances between the input points. However, current state-of-the-art approaches only scale to a few thousand data points. For larger data sets such as those occurring in single-cell RNA sequencing experiments, the running time becomes prohibitively large and thus alternative methods such as PCA are widely used instead. Here, we propose a simple neural network-based approach for solving the metric multidimensional scaling problem that is orders of magnitude faster than previous state-of-the-art approaches, and hence scales to data sets with up to a few million cells. At the same time, it provides a non-linear mapping between high- and low-dimensional space that can place previously unseen cells in the same embedding.

Exploration of tissue fixation methods suitable for digital pathological studies of the testis

BACKGROUND

The way of testicular tissue fixation directly affects the correlation and structural integrity between connective tissue and seminiferous tubules, which is essential for the study of male reproductive development. This study aimed to find the optimal fixative and fixation time to produce high-quality testicular histopathological sections, and provided a suitable foundation for in-depth study of male reproductive development with digital pathology technology.

METHODS

Testes were removed from both sides of 25 male C57BL/6 mice. Samples were fixed in three different fixatives, 10% neutral buffered formalin (10% NBF), modified Davidson's fluid (mDF), and Bouin's Fluid (BF), for 8, 12, and 24 h, respectively. Hematoxylin and eosin (H&E) staining, periodic acid Schiff-hematoxylin (PAS-h) staining, and immunohistochemistry (IHC) were used to evaluate the testicle morphology, staging of mouse seminiferous tubules, and protein preservation. Aperio ScanScope CS2 panoramic scanning was used to perform quantitative analyses.

RESULTS

H&E staining showed 10% NBF resulted in an approximately 15-17% reduction in the thickness of seminiferous epithelium. BF and mDF provided excellent results when staining acrosomes with PAS-h. IHC staining of synaptonemal complexes 3 (Sycp3) was superior in mDF compared to BF-fixed samples. Fixation in mDF and BF improved testis tissue morphology compared to 10% NBF.

CONCLUSIONS

Quantitative analysis showed that BF exhibited a very low IHC staining efficiency and revealed that mouse testes fixed for 12 h with mDF, exhibited morphological details, excellent efficiency of PAS-h staining for seminiferous tubule staging, and IHC results. In addition, the morphological damage of testis was prolonged with the duration of fixation time.

Spatial omics techniques and data analysis for cancer immunotherapy applications

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

A practical guide to spatial transcriptomics

Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.

Spatial Patterning Analysis of Cellular Ensembles (SPACE) discovers complex spatial organization at the cell and tissue levels

Spatial patterns of cells and other biological elements drive both physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple elements in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high-plex spatial data. SPACE is compatible with any data collection modality that records values (i.e., categorical cell/structure types or quantitative expression levels) at fixed spatial coordinates (i.e., 2d pixels or 3d voxels). SPACE detects not only broad patterns of co-occurrence but also context-dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles of tissue elements - single elements, pairs, triplets, and so on - and ranks the most strongly patterned ensembles. For single images, rankings reflect patterns that differ from random assortment. For sets of images, rankings reflect patterns that differ across sample groups (e.g., genotypes, treatments, timepoints, etc.). Further tools then thoroughly characterize the nature of each pattern for intuitive interpretation. We validate SPACE and demonstrate its advantages using murine lymph node images for which ground truth has been defined. We then use SPACE to detect new patterns across varied datasets, including tumors and tuberculosis granulomas.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Heterogeneity in Liver Cancer Immune Microenvironment: Emerging Single-Cell and Spatial Perspectives

Primary liver cancer is a solid malignancy with a high mortality rate. The success of immunotherapy has shown great promise in improving patient care and highlights a crucial need to understand the complexity of the liver tumor immune microenvironment (TIME). Recent advances in single-cell and spatial omics technologies, coupled with the development of systems biology approaches, are rapidly transforming the landscape of tumor immunology. Here we review the cellular landscape of liver TIME from single-cell and spatial perspectives. We also discuss the cellular interaction networks within the tumor cell community in regulating immune responses. We further highlight the challenges and opportunities with implications for biomarker discovery, patient stratification, and combination immunotherapies.

Complete spatially resolved gene expression is not necessary for identifying spatial domains

Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.

Clinicogenomic characterization of inflammatory breast cancer

Background

Inflammatory breast cancer (IBC) is a rare and poorly characterized type of breast cancer with an aggressive clinical presentation. The biological mechanisms driving the IBC phenotype are relatively undefined-partially due to a lack of comprehensive, large-scale genomic studies and limited clinical cohorts.

Patients and Methods

A retrospective analysis of 2457 patients with metastatic breast cancer who underwent targeted tumor-only DNA-sequencing was performed at Dana-Farber Cancer Institute. Clinicopathologic, single nucleotide variant (SNV), copy number variant (CNV) and tumor mutational burden (TMB) comparisons were made between clinically confirmed IBC cases within a dedicated IBC center versus non-IBC cases.

Results

Clinicopathologic differences between IBC and non-IBC cases were consistent with prior reports-including IBC being associated with younger age at diagnosis, higher grade, and enrichment with hormone receptor (HR)-negative and HER2-positive tumors. The most frequent somatic alterations in IBC involved TP53 (72%), ERBB2 (32%), PIK3CA (24%), CCND1 (12%), MYC (9%), FGFR1 (8%) and GATA3 (8%). A multivariate logistic regression analysis revealed a significant enrichment in TP53 SNVs in IBC; particularly in HER2-positive and HR-positive disease which was associated with worse outcomes. Tumor mutational burden (TMB) did not differ substantially between IBC and non-IBC cases and a pathway analysis revealed an enrichment in NOTCH pathway alterations in HER2-positive disease.

Conclusion

Taken together, this study provides a comprehensive, clinically informed landscape of somatic alterations in a large cohort of patients with IBC. Our data support higher frequency of TP53 mutations and a potential enrichment in NOTCH pathway activation-but overall; a lack of major genomic differences. These results both reinforce the importance of TP53 alterations in IBC pathogenesis as well as their influence on clinical outcomes; but also suggest additional analyses beyond somatic DNA-level changes are warranted.

scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data

Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.

scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer

Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.

An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus

The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and infer the expression of these patterns in the SRT data. With this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.

Increased Multiplexity in Optical Tissue Clearing-Based Three-Dimensional Immunofluorescence Microscopy of the Tumor Microenvironment by Light-Emitting Diode Photobleaching

Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy is transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only 3 or 4 cellular and noncellular TME components can be localized in cleared tumor tissue. Here we report a light-emitting diode (LED) photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through 3 work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.

Charting the cellular biogeography in colitis reveals fibroblast trajectories and coordinated spatial remodeling

Gut inflammation involves contributions from immune and non-immune cells, whose interactions are shaped by the spatial organization of the healthy gut and its remodeling during inflammation. The crosstalk between fibroblasts and immune cells is an important axis in this process, but our understanding has been challenged by incomplete cell-type definition and biogeography. To address this challenge, we used multiplexed error-robust fluorescence in situ hybridization (MERFISH) to profile the expression of 940 genes in 1.35 million cells imaged across the onset and recovery from a mouse colitis model. We identified diverse cell populations, charted their spatial organization, and revealed their polarization or recruitment in inflammation. We found a staged progression of inflammation-associated tissue neighborhoods defined, in part, by multiple inflammation-associated fibroblasts, with unique expression profiles, spatial localization, cell-cell interactions, and healthy fibroblast origins. Similar signatures in ulcerative colitis suggest conserved human processes. Broadly, we provide a framework for understanding inflammation-induced remodeling in the gut and other tissues.

The covariance environment defines cellular niches for spatial inference

A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.

Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling

PURPOSE OF REVIEW

Single-cell profiling, either in suspension or within the tissue context, is a rapidly evolving field. The purpose of this review is to outline recent advancements and emerging trends with a specific focus on studies in spondyloarthritis.

RECENT FINDINGS

The introduction of sequencing-based approaches for the quantification of RNA, protein, or epigenetic modifications at single-cell resolution has provided a major boost to discovery-driven research. Fluorescent flow cytometry, mass cytometry, and image-based cytometry continue to evolve. Spatial transcriptomics and imaging mass cytometry have extended high-dimensional analysis to cells in tissues. Applications in spondyloarthritis include the indexing and functional characterization of cells, discovery of disease-associated cell states, and identification of signatures associated with therapeutic responses. Single-cell TCR-seq has provided evidence for clonal expansion of CD8+ T cells in spondyloarthritis. The use of single-cell profiling approaches in spondyloarthritis research is still in its early stages. Challenges include high cost and limited availability of diseased tissue samples. To harness the full potential of the rapidly expanding technical capabilities, large-scale collaborative efforts are imperative.

Benchmarking spatial clustering methods with spatially resolved transcriptomics data

Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.

In situ characterization of the tumor microenvironment

The development of new therapies for cancer is underpinned by an increasing need to comprehensively characterize the tumor microenvironment (TME). While traditional approaches have relied on bulk or single-cell approaches, these are limited in their ability to provide cellular context. Deconvolution of the complex TME is fundamental to understanding tumor dynamics and treatment resistance. Spatially resolved characterization of the TME is likely to provide greater insights into the cellular architecture, tumor-immune cell interactions, receptor-ligand interactions, and cell niches. In turn, these aid in dictating the optimal way in which to target each patient's individual cancer. In this review, we discuss a number of cutting-edge in situ spatial profiling methods giving us new insights into tumor biology.

Approaches to probe and perturb long noncoding RNA functions in diseases

Long noncoding RNAs (lncRNAs) are a class of RNA molecules exceeding 200 nucleotides in length that lack long open-reading frames. Transcribed predominantly by RNA polymerase II (>500nt), lncRNAs can undergo splicing and are produced from various regions of the genome, including intergenic regions, introns, and in antisense orientation to protein-coding genes. Aberrations in lncRNA expression or function have been associated with a wide variety of diseases, including cancer, cardiovascular diseases, diabetes, and neurodegeneration. Despite the growing recognition of select lncRNAs as key players in cellular processes and diseases, several challenges obscure a comprehensive understanding of their functional landscape. Recent technological innovations, such as in sequencing, affinity-based techniques, imaging, and RNA perturbation, have advanced functional characterization and mechanistic understanding of disease-associated lncRNAs.

The follicular lymphoma tumor microenvironment at single-cell and spatial resolution

ABSTRACT

Follicular lymphoma (FL) is a generally incurable malignancy that originates from developmentally blocked germinal center B cells residing, primarily, within lymph nodes (LNs). During the long natural history of FL, malignant B cells often disseminate to multiple LNs and can affect virtually any organ. Nonmalignant LNs are highly organized structures distributed throughout the body, in which they perform functions critical for host defense. In FL, the malignant B cells "re-educate" the lymphoid environment by altering the phenotype, distribution, and abundance of other cells such as T cells, macrophages, and subsets of stromal cells. Consequently, dramatic anatomical changes occur and include alterations in the number, shape, and size of neoplastic follicles with an accompanying attenuation of the T-cell zone. Ongoing and dynamic interactions between FL B cells and the tumor microenvironment (TME) result in significant clinical heterogeneity observed both within and across patients. Over time, FL evolves into pathological variants associated with distinct outcomes, ranging from an indolent disease to more aggressive clinical courses with early death. Given the importance of both cell-intrinsic and -extrinsic factors in shaping disease progression and patient survival, comprehensive examination of FL tumors is critical. Here, we describe the cellular composition and architecture of normal and malignant human LNs and provide a broad overview of emerging technologies for deconstructing the FL TME at single-cell and spatial resolution. We additionally discuss the importance of capturing samples at landmark time points as well as longitudinally for clinical decision-making.

SpatialData: an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.

Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes

High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present Fluorescence In Situ Hybridization of Cellular HeterogeneIty and gene expression Programs (FISHnCHIPs) for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes (clustered into modules) that are spatially co-localized in tissues, resulting in similar spatial information as single-gene Fluorescence In Situ Hybridization (FISH), but with ~2-20-fold higher sensitivity. Using FISHnCHIPs, we image up to 53 modules from the mouse kidney and mouse brain, and demonstrate high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPs also reveals spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables fast, robust, and scalable cell typing of tissues with normal physiology or undergoing pathogenesis.

A Foundation Model for Cell Segmentation

Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models in use are specialist models that work well for specific domains. Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive. In this work, we present CellSAM, a foundation model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells (in tissues and cell culture), yeast, and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. We also show that CellSAM can unify bioimaging analysis workflows such as spatial transcriptomics and cell tracking. A deployed version of CellSAM is available at https://cellsam.deepcell.org/.

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

Streamlining spatial omics data analysis with Pysodb

Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.

Translation is an emerging constraint on protein homeostasis in ageing

Proteins are molecular machines that provide structure and perform vital transport, signalling and enzymatic roles. Proteins expressed by cells require tight regulation of their concentration, folding, localisation, and modifications; however, this state of protein homeostasis is continuously perturbed by tissue-level stresses. While cells in healthy tissues are able to buffer against these perturbations, for example, by expression of chaperone proteins, protein homeostasis is lost in ageing, and can lead to protein aggregation characteristic of protein folding diseases. Here, we review reports of a progressive disconnect between transcriptomic and proteomic regulation during cellular ageing. We discuss how age-associated changes to cellular responses to specific stressors in the tissue microenvironment are exacerbated by loss of ribosomal proteins, ribosomal pausing, and mistranslation.

scGIST: gene panel design for spatial transcriptomics with prioritized gene sets

A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST's efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST's algorithmic toolbox.

Extended correlation functions for spatial analysis of multiplex imaging data

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

A perspective on FAIR quality control in multiplexed imaging data processing

Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.

Biomass-related PM2.5 induced inflammatory microenvironment via IL-17F/IL-17RC axis

Biomass exposure is a significant environmental risk factor for COPD, but the underlying mechanisms have not yet been fully elucidated. Inflammatory microenvironment has been shown to drive the development of many chronic diseases. Pollution exposure can cause increased levels of inflammatory factors in the lungs, leading to an inflammatory microenvironment which is prevalent in COPD. Our findings revealed that IL-17F was elevated in COPD, while exposure to biomass led to increased expression of IL-17F in both alveolar epithelial and macrophage cells in mice. Blocking IL-17F could alleviate the lung inflammation induced by seven days of biomass exposure in mice. We employed a transwell co-culture system to simulate the microenvironment and investigate the interactions between MLE-12 and MH-S cells. We demonstrated that anti-IL-17F antibody attenuated the inflammatory responses induced by BRPM2.5 in MLE-12 and MH-S co-cultured with BRPM2.5-MLE-12, which reduced inflammatory changes in microenvironment. We found that IL-17RC, an important receptor for IL-17F, played a key role in the interactions. Knockout of IL-17RC in MH-S resulted in inhibited IL-17F signaling and attenuated inflammatory response after MH-S co-culture with BRPM2.5-MLE-12. Our investigation suggests that BRPM2.5 induces lung epithelial-macrophage interactions via IL-17F/IL-17RC axis regulating the inflammatory response. These results may provide a novel strategy for effective prevention and treatment of biomass-related COPD.

Lung regeneration: diverse cell types and the therapeutic potential

Lung tissue has a certain regenerative ability and triggers repair procedures after injury. Under controllable conditions, lung tissue can restore normal structure and function. Disruptions in this process can lead to respiratory system failure and even death, causing substantial medical burden. The main types of respiratory diseases are chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), and acute respiratory distress syndrome (ARDS). Multiple cells, such as lung epithelial cells, endothelial cells, fibroblasts, and immune cells, are involved in regulating the repair process after lung injury. Although the mechanism that regulates the process of lung repair has not been fully elucidated, clinical trials targeting different cells and signaling pathways have achieved some therapeutic effects in different respiratory diseases. In this review, we provide an overview of the cell type involved in the process of lung regeneration and repair, research models, and summarize molecular mechanisms involved in the regulation of lung regeneration and fibrosis. Moreover, we discuss the current clinical trials of stem cell therapy and pharmacological strategies for COPD, IPF, and ARDS treatment. This review provides a reference for further research on the molecular and cellular mechanisms of lung regeneration, drug development, and clinical trials.

Advances and opportunities in methods to study protein translation - A review

It is generally believed that the regulation of gene expression involves protein translation occurring before RNA transcription. Therefore, it is crucial to investigate protein translation and its regulation. Recent advancements in biological sciences, particularly in the field of omics, have revolutionized protein translation research. These studies not only help characterize changes in protein translation during specific biological or pathological processes but also have significant implications in disease prevention and treatment. In this review, we summarize the latest methods in ribosome-based translation omics. We specifically focus on the application of fluorescence imaging technology and omics technology in studying overall protein translation. Additionally, we analyze the advantages, disadvantages, and application of these experimental methods, aiming to provide valuable insights and references to researchers studying translation.

Piscis: a novel loss estimator of the F1 score enables accurate spot detection in fluorescence microscopy images via deep learning

Single-molecule RNA fluorescence in situ hybridization (RNA FISH)-based spatial transcriptomics methods have enabled the accurate quantification of gene expression at single-cell resolution by visualizing transcripts as diffraction-limited spots. While these methods generally scale to large samples, image analysis remains challenging, often requiring manual parameter tuning. We present Piscis, a fully automatic deep learning algorithm for spot detection trained using a novel loss function, the SmoothF1 loss, that approximates the F1 score to directly penalize false positives and false negatives but remains differentiable and hence usable for training by deep learning approaches. Piscis was trained and tested on a diverse dataset composed of 358 manually annotated experimental RNA FISH images representing multiple cell types and 240 additional synthetic images. Piscis outperforms other state-of-the-art spot detection methods, enabling accurate, high-throughput analysis of RNA FISH-derived imaging data without the need for manual parameter tuning.

Generating detailed intercellular communication patterns in psoriasis at the single-cell level using social networking, pattern recognition, and manifold learning methods to optimize treatment strategies

Psoriasis, a complex and recurrent chronic inflammatory skin disease involving various inflammatory cell types, requires effective cell communication to maintain the homeostatic balance of inflammation. However, patterns of communication at the single-cell level have not been systematically investigated. In this study, we employed social network analysis tools, pattern recognition, and manifold learning to compare molecular communication features between psoriasis cells and normal skin cells. Utilizing a process that facilitates the discovery of cell type-specific regulons, we analyzed internal regulatory networks among different cells in psoriasis. Advanced techniques for the quantitative detection of non-targeted proteins in pathological tissue sections were employed to demonstrate protein expression. Our findings revealed a synergistic interplay among the communication signals of immune cells in psoriasis. B-cells were activated, while Langerhans cells shifted into the primary signaling output mode to fulfill antigen presentation, mediating T-cell immunity. In contrast to normal skin cells, psoriasis cells shut down numerous signaling pathways, influencing the balance of skin cell renewal and differentiation. Additionally, we identified a significant number of active cell type-specific regulons of resident immune cells around the hair follicle. This study unveiled the molecular communication features of the hair follicle cell-psoriasis axis, showcasing its potential for therapeutic targeting at the single-cell level. By elucidating the pattern of immune cell communication in psoriasis and identifying new molecular features of the hair follicle cell-psoriasis axis, our findings present innovative strategies for drug targeting to enhance psoriasis treatment.

Deciphering Drug Targets and Actions with Single-Cell and Spatial Resolution

Recent advances in chemical, molecular, and genetic approaches have provided us with an unprecedented capacity to identify drug-target interactions across the whole proteome and genome. Meanwhile, rapid developments of single-cell and spatial omics technologies are revolutionizing our understanding of the molecular architecture of biological systems. However, a significant gap remains in how we align our understanding of drug actions, traditionally based on molecular affinities, with the in vivo cellular and spatial tissue heterogeneity revealed by these newer techniques. Here, we review state-of-the-art methods for profiling drug-target interactions and emerging multiomics tools to delineate the tissue heterogeneity at single-cell resolution. Highlighting the recent technical advances enabling high-resolution, multiplexable in situ small-molecule drug imaging (clearing-assisted tissue click chemistry, or CATCH), we foresee the integration of single-cell and spatial omics platforms, data, and concepts into the future framework of defining and understanding in vivo drug-target interactions and mechanisms of actions.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

STAB2: an updated spatio-temporal cell atlas of the human and mouse brain

The brain is constituted of heterogeneous types of neuronal and non-neuronal cells, which are organized into distinct anatomical regions, and show precise regulation of gene expression during development, aging and function. In the current database release, STAB2 provides a systematic cellular map of the human and mouse brain by integrating recently published large-scale single-cell and single-nucleus RNA-sequencing datasets from diverse regions and across lifespan. We applied a hierarchical strategy of unsupervised clustering on the integrated single-cell transcriptomic datasets to precisely annotate the cell types and subtypes in the human and mouse brain. Currently, STAB2 includes 71 and 61 different cell subtypes defined in the human and mouse brain, respectively. It covers 63 subregions and 15 developmental stages of human brain, and 38 subregions and 30 developmental stages of mouse brain, generating a comprehensive atlas for exploring spatiotemporal transcriptomic dynamics in the mammalian brain. We also augmented web interfaces for querying and visualizing the gene expression in specific cell types. STAB2 is freely available at https://mai.fudan.edu.cn/stab2.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

BACKGROUND

Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R.

RESULTS

Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples.

CONCLUSIONS

The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .

CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity

Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.

Spatial omics: An innovative frontier in aging research

Disentangling the impact of aging on health and disease has become critical as population aging progresses rapidly. Studying aging at the molecular level is complicated by the diverse aging profiles and dynamics. However, the examination of cellular states within aging tissues in situ is hampered by the lack of high-resolution spatial data. Emerging spatial omics technologies facilitate molecular and spatial analysis of tissues, providing direct access to precise information on various functional regions and serving as a favorable tool for unraveling the heterogeneity of aging. In this review, we summarize the recent advances in spatial omics application in multi-organ aging research, which has enhanced the understanding of aging mechanisms from multiple standpoints. We also discuss the main challenges in spatial omics research to date, the opportunities for further developing the technology, and the potential applications of spatial omics in aging and aging-related diseases.

Single-Molecule Fluorescent In Situ Hybridization (smFISH) for RNA Detection in the Fungal Pathogen Candida albicans

Candida albicans is the most prevalent human fungal pathogen. Its pathogenicity is linked to the ability of C. albicans to reversibly change morphology and to grow as yeast, pseudohyphae, or hyphal cells in response to environmental stimuli. Understanding the molecular regulation controlling those morphological switches remains a challenge that, if solved, could help eradicate C. albicans infections.While numerous studies investigated gene expression changes occurring during C. albicans morphological switches using bulk approaches (e.g., RNA sequencing), here we describe a single-cell and single-molecule RNA imaging and analysis protocol to measure absolute mRNA counts in morphologically intact cells. To detect endogenous mRNAs in single fixed cells, we optimized a single-molecule fluorescent in situ hybridization (smFISH) protocol for C. albicans, which allows one to quantify the differential expression of mRNAs in yeast, pseudohyphae, or hyphal cells. We quantified the expression of two mRNAs, a cell cycle-controlled mRNA (CLB2) and a transcription factor (EFG1), which show expression changes in the different morphological cell types and nutrient conditions. In this protocol, we described in detail the major steps of this approach: growth and fixation, hybridization, imaging, cell segmentation, and mRNA spot analysis. Raw data is provided with the protocol to favor reproducibility. This approach could benefit the molecular characterization of C. albicans and other filamentous fungi, pathogenic or nonpathogenic.

Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data

Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution reveals plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allows for spatial mapping of reference populations with significantly reduced ambiguity.

SingleScan: a comprehensive resource for single-cell sequencing data processing and mining

Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .

Increased multiplexity in optical tissue clearing-based 3D immunofluorescence microscopy of the tumor microenvironment by LED photobleaching

Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy have been transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only three or four cellular and non-cellular TME components can be localized in a cleared tumor tissue. Here we report a LED photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through three work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.

Dimension-agnostic and granularity-based spatially variable gene identification using BSP

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.