High-definition spatial transcriptomics for in situ tissue profiling

CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells' molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions.

Scalable image-based visualization and alignment of spatial transcriptomics datasets

We present the "spatial transcriptomics imaging framework" (STIM), an imaging-based computational framework focused on visualizing and aligning high-throughput spatial sequencing datasets. STIM is built on the powerful, scalable ImgLib2 and BigDataViewer (BDV) image data frameworks and thus enables novel development or transfer of existing computer vision techniques to the sequencing domain characterized by datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIM's capabilities by representing, interactively visualizing, 3D rendering, automatically registering, and segmenting publicly available spatial sequencing data from 13 serial sections of mouse brain tissue and from 19 sections of a human metastatic lymph node. We demonstrate that the simplest alignment mode of STIM achieves human-level accuracy.

Potential of queen bee larvae as a dietary supplement for obesity management: modulating the gut microbiota and promoting liver lipid metabolism

Queen bee larvae (QBL) have been consumed as both a traditional food and medicine in China for thousands of years; however, their specific benefits for human health, particularly their potential anti-obesity property, remain underexplored. This study investigated the anti-obesity effect of QBL freeze-dried powder (QBLF) on high-fat diet (HFD) induced obesity in mice and explored the underlying mechanisms. Our findings showed that QBLF effectively reduced body weight, fasting blood glucose levels, lipid accumulation, and inflammation in HFD mice. 16S rRNA sequencing revealed that QBLF significantly modulated the gut microbiota disrupted by an HFD, notably increasing the relative abundance of beneficial microbes such as Ileibacterium, Clostridium sensu stricto 1, Incertae sedis, Streptococcus, Lactococcus, Clostridia UCG-014, and Lachnospiraceae UCG-006, which were inversely associated with obesity-related phenotypes in the mice. RNA sequencing analysis further demonstrated that QBLF intervention upregulated the expression of genes involved in liver lipid metabolism, including Pck1, Cyp4a10, Cyp4a14, and G6pc, while downregulating genes associated with the inflammatory response, such as Cxcl10, Ccl2, Traf1, Mapk15, Lcn2, and Fosb. These results suggested that QBLF can ameliorate HFD-induced obesity through regulating the gut microbiota, promoting liver lipid metabolism, and reducing inflammatory response.

Spatial Insights in Cardiovascular Aging

Spatial omics provides unprecedented insights into how the cardiovascular system is spatially organized and how cellular phenotypes are distributed. Researchers have been able to clarify the complex spatial architecture of the cardiovascular system and how cellular phenotypes are distributed during the aging process by integrating data from spatial omics. In addition, this new technology has revealed previously hidden patterns of gene expression and cellular communication that were not detected using traditional bulk omics approaches. In this review, we explore the contribution of spatial omics in clarifying the molecular mechanisms that influence cardiovascular aging, highlighting the importance and application of spatial omics in unraveling the spatial heterogeneity within the aging cardiovascular system. This will help us understand the molecular mechanisms implicated in age-related cardiovascular diseases.

Advances in spatial transcriptomics and its application in the musculoskeletal system

While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states, the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation. Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues, gradually finding applications in musculoskeletal research. This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system. The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined, with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs, accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research. The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system, particularly during developmental processes, is thoroughly summarized. Furthermore, recent discoveries and achievements of this emerging omics tool in addressing inflammatory, traumatic, degenerative, and tumorous diseases of the musculoskeletal system are compiled. Finally, challenges and potential future directions for spatial transcriptomics, both as a field and in its applications in the musculoskeletal system, are discussed.

Performance of MRI-based radiomics for prediction of residual disease status in patients with nasopharyngeal carcinoma after radical radiotherapy

The purpose of this study was to determine if habitat radiomic features extracted from pretherapy multi-sequence MRI predict residual status in patients with Nasopharyngeal Carcinoma (NPC) after radical radiotherapy. The retrospective study enrolled 179 primary NPC patients, divided into training and validation cohorts at a 7:3 ratio. K-means clustering was employed to segment T2WI, CE-T1WI and FSCE-T1WI images, creating habitats within the volume of interest. Identify relevant features that can recognize NPC residuals. In the training cohort, support vector machine (SVM) models were developed utilizing the radiomic features extracted from each habitat and the entire tumor, selecting the most predictive features for each sequence. SVM models were constructed by combining the optimal radiomic features from each sequences with clinical data. Model performance was compared and validated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA), and differences between models were assessed using the DeLong test. The optimal clustering results revealed 4 habitats in FSCE-T1WI, while 2 habitats in both CE-T1WI and T2WI sequences. In the training cohort, we compared the predictive accuracy of SVM models based on different habitats and total tumor characteristics from three sequences, and found that the features from T2 Hab2, CE-T1 Hab1, and FSCE-T1 Hab4 images showed higher performance. Incorporation of habitat-based radiomic features and clinical variables significantly enhanced the predictive performance. The integrated model exhibits the optimal predictive performance, with the area under the curve (AUC) values of 0.921 (SEN = 0.821, SPE = 0.830) in the training cohort and 0.811 (SEN = 0.778, SPE = 0.722) in the validation cohort. Compared to conventional radiomics, habitat imaging features that distinguish intratumoral heterogeneity have higher predictive value, making them potential non-invasive biomarkers for assessing NPC residual after radiotherapy. Integration of multi-sequence MRI habitat radiomic with clinical parameters further improved predictive accuracy.

Spatial transcriptomic applications in orthopedics

PURPOSE

This review highlights the transformative impact of spatial transcriptomics on orthopedic research, focusing on its application in deciphering intricate gene expression patterns within musculoskeletal tissues.

METHODS

The paper reviews literature for spatial transcriptomic methods, specifically 10X Visium, 10X Xenium, seqFISH+, MERFISH, NanoString GeoMx DSP, used on musculoskeletal tissues (cartilage, joints, bone, tendon, ligament, and synovium).

RESULTS

Searches identified 29 published manuscripts reporting spatial transcriptomic data in cartilage, bone, tendon, synovium, and intervertebral disc. Most publications of spatial transcriptomic data are from tendon and synovium. 10X Visium has been used in 22 of the 29 papers identified. Spatial transcriptomics has been used to identify novel cell populations and cell signaling pathways that regulate development and disease.

CONCLUSIONS

Imaging-based spatial transcriptomic methods may be better for hypothesis testing as they generally have subcellular resolution but sequence fewer genes. Sequencing methods may be better for untargeted, shotgun approaches that can generate useful hypotheses from the spatial data from unimpaired tissue sections. Spatial transcriptomic methods could become useful for clinical diagnostics and precision medicine approaches.

Spatial transcriptomics reveals tumor microenvironment heterogeneity in EBV positive diffuse large B cell lymphoma

Accumulating research suggests that Epstein-Barr Virus-positive Diffuse Large B-cell Lymphoma (EBV+DLBCL) is associated with immune dysfunction and tumor microenvironment (TME) heterogeneity. While the prognostic role of the TME in EBV-DLBCL is established, its impact on EBV+DLBCL survival remains unclear. Here, we integrated 10X Visium spatial transcriptomics (ST) with single-cell RNA sequencing (scRNA-seq) to map TME heterogeneity in EBV+DLBCL. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses identified PD-1/PD-L1 signaling as a hallmark of EBV+DLBCL's immunosuppressive TME. Functional validation using the PD-1/PD-L1 inhibitor BMS202 revealed dose-dependent suppression of proliferation and enhanced apoptosis in EBV+Farage cells, with TLR4 emerging as a downstream effector showing EBV-status-dependent regulation. These findings not only link TME-driven PD-1/PD-L1 activation to EBV+DLBCL's poor prognosis but also provide preclinical evidence for PD-1/PD-L1 blockade as a therapeutic strategy.

Emerging Techniques in Spatial Multiomics: Fundamental Principles and Applications to Dermatology

Molecular pathology, such as high-throughput genomic and proteomic profiling, identifies precise disease targets from biopsies but require tissue dissociation, losing valuable histologic and spatial context. Emerging spatial multi-omic technologies now enable multiplexed visualization of genomic, proteomic, and epigenomic targets within a single tissue slice, eliminating the need for labeling multiple adjacent slices. Although early work focused on RNA (spatial transcriptomics), spatial technologies can now concurrently capture DNA, genome accessibility, histone modifications, and proteins with spatially-resolved single-cell resolution. This review outlines the principles, advantages, limitations, and potential for spatial technologies to advance dermatologic research. By jointly profiling multiple molecular channels, spatial multiomics enables novel studies of copy number variations, clonal heterogeneity, and enhancer dysregulation, replete with spatial context, illuminating the skin's complex heterogeneity.

Advances in Spatial Omics Technologies

Rapidly developing spatial omics technologies provide us with new approaches to deeply understanding the diversity and functions of cell types within organisms. Unlike traditional approaches, spatial omics technologies enable researchers to dissect the complex relationships between tissue structure and function at the cellular or even subcellular level. The application of spatial omics technologies provides new perspectives on key biological processes such as nervous system development, organ development, and tumor microenvironment. This review focuses on the advancements and strategies of spatial omics technologies, summarizes their applications in biomedical research, and highlights the power of spatial omics technologies in advancing the understanding of life sciences related to development and disease.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

A spatially resolved transcriptome landscape during thyroid cancer progression

Tumor microenvironment (TME) remodeling plays a pivotal role in thyroid cancer progression, yet its spatial dynamics remain unclear. In this study, we integrate spatial transcriptomics and single-cell RNA sequencing to map the TME architecture across para-tumor thyroid (PT) tissue, papillary thyroid cancer (PTC), locally advanced PTC (LPTC), and anaplastic thyroid carcinoma (ATC). Our integrative analysis reveals extensive molecular and cellular heterogeneity during thyroid cancer progression, enabling the identification of three distinct thyrocyte meta-clusters, including TG+IYG+ subpopulation in PT, HLA-DRB1+HLA-DRA+ subpopulation in early cancerous stages, and APOE+APOC1+ subpopulation in late-stage progression. We reveal stage-specific tumor leading edge remodeling and establish high-confidence cell-cell interactions, such as COL8A1-ITHB1 in PTC, LAMB2-ITGB4 in LPTC, and SERPINE1-PLAUR in ATC. Notably, both SERPINE1 expression level and SERPINE1+ fibroblast abundance correlate with malignant progression and prognosis. These findings provide a spatially resolved framework of TME remodeling, offering insights for thyroid cancer diagnosis and treatment.

Spatial omics for accelerating plant research and crop improvement

Plant cells communicate information to regulate developmental processes and respond to environmental stresses. This communication spans various 'omics' layers within a cell and operates through intricate regulatory networks. The emergence of spatial omics presents a promising approach to thoroughly analyze cells, allowing the combined analysis of diverse modalities either in parallel or on the same tissue section. Here, we provide an overview of recent advancements in spatial omics and delineate scientific discoveries in plant research enabled by these technologies. We delve into experimental and computational challenges and outline strategies to navigate these challenges for advancing breeding efforts. With ongoing insightful discoveries and improved accessibility, spatial omics stands on the brink of playing a crucial role in designing future crops.

Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder

Domain identification is a critical problem in spatially resolved transcriptomics data analysis, which aims to identify distinct spatial domains within a tissue that maintain both spatial continuity and expression consistency. The degree of coupling between expression data and spatial information in different datasets often varies significantly. Some regions have intact and clear boundaries, while others exhibit blurred boundaries with high intra-domain expression similarity. However, most domain identification methods do not adequately integrate expression and spatial information to flexibly identify different types of domains. To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. Spot2vector encodes and integrates spatial and expression information, enabling effective identification of domains with diverse spatial patterns across spatially resolved transcriptomics data generated by different platforms. The decoders enable us to decipher the distribution and generation mechanisms of data while improving expression quality through denoising. Extensive validation and analyses demonstrate that Spot2vector excels in enhancing domain identification accuracy, effectively reducing data dimensionality, improving expression recovery and denoising, and precisely capturing spatial gene expression patterns.

Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics

The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis and disease. Here, we survey the current landscape of microfluidic methods in the field of single-cell and spatial multi-omics, as well as assessing their relative advantages and limitations. We highlight how microfluidics has been adapted and improved to provide new insights into multi-omics over the past decade. Last, we emphasize the contributions of microfluidic-based omics methods in development, neuroscience, and disease mechanisms, as well as further revealing some perspectives for technological advances in translational and clinical medicine.

Assessing the virological response to direct-acting antiviral therapies in the HBV cure programme

Hepatitis B virus (HBV) is a global health problem with over 250 million people affected worldwide. Nucleos(t)ide analogues remain the standard of care and suppress production of progeny virions; however, they have limited effect on the viral transcriptome and long-term treatment is associated with off-target toxicities. Promising results are emerging from clinical trials and several drug classes have been evaluated, including capsid assembly modulators and RNA interfering agents. Whilst peripheral biomarkers are used to monitor responses and define treatment endpoints, they fail to reflect the full reservoir of infected hepatocytes. Given these limitations, consideration should be given to the merits of sampling liver tissue, especially in the context of clinical trials. In this review article, we will discuss methods for profiling HBV in liver tissue and their value to the HBV cure programme.

STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data

Unraveling the spatial configuration of the tumor microenvironment (TME) is crucial for elucidating tumor-immune interactions based on immuno-oncology. We present STopover, a novel approach utilizing spatially resolved transcriptomics (SRT) data and topological analysis to investigate the TME. By gradually lowering the feature threshold, connected components (CCs) are extracted based on spatial distance and persistence, with Jaccard indices quantifying their spatial overlap, and transcriptomic profiles are permutated to assess statistical significance. Applied to lung and breast cancer SRT, STopover revealed immune and stromal cell infiltration patterns, predicted key cell-cell communication, and identified relevant regions, shedding light on cancer pathophysiology (URL: https://github.com/bsungwoo/STopover ).

Large-scale combinatorial optical barcoding of cells with laser particles

The identification of individual cells is crucial for advancements in single-cell analysis. Optically readable barcodes provide a means to distinguish and track cells through repeated, non-destructive measurements. Traditional fluorophore-based methods are limited by the finite number of unique barcodes they can produce. Laser particles (LPs), which emit narrowband peaks over a wide spectral range, have emerged as a promising technology for single-cell barcoding. Here, we demonstrate the use of multiple LPs to generate combinatorial barcodes, enabling the identification of a vast number of live cells. We introduce a theoretical framework for estimating the number of LPs required for unique barcodes and the expected identification error rate. Additionally, we present an improved LP-tagging method that is highly effective across a variety of cell types and evaluate its biocompatibility. Our experimental results show successful barcoding of several million cells, closely matching our theoretical predictions. This research marks a significant step forward in the scalability of LP technology for single-cell tracking and analysis.

Multiscale Dissection of Spatial Heterogeneity by Integrating Multi-Slice Spatial and Single-Cell Transcriptomics

The spatial structure of cells is highly organized at multiscale levels from global spatial domains to local cell type heterogeneity. Existing methods for analyzing spatially resolved transcriptomics (SRT) are separately designed for either domain alignment across multiple slices or deconvoluting cell type compositions within a single slice. To this end, a novel deep learning method, SMILE, is proposed which combines graph contrastive autoencoder and multilayer perceptron with local constraints to learn multiscale and informative spot representations. By comparing SMILE with the state-of-the-art methods on simulation and real datasets, the superior performance of SMILE is demonstrated on spatial alignment, domain identification, and cell type deconvolution. The results show SMILE's capability not only in simultaneously dissecting spatial variations at different scales but also in unraveling altered cellular microenvironments in diseased conditions. Moreover, SMILE can utilize prior domain annotation information of one slice to further enhance the performance.

Sequencing-free whole genome spatial transcriptomics at molecular resolution in intact tissue

Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, compositions, interactions, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution. Leading spatial-capture or spatial-tagging transcriptomics techniques that rely on in-vitro sequencing offer whole-transcriptome coverage, in principle, but at the cost of lower spatial resolution compared to image-based techniques. In contrast, high-performance image-based spatial transcriptomics techniques, which rely on in situ hybridization or in situ sequencing, achieve single-molecule spatial resolution and retain sub-cellular morphologies, but are limited by probe libraries that target only a subset of the transcriptome, typically covering several hundred to a few thousand transcript species. Together, these limitations hinder unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop a new image-based spatial transcriptomics technology termed Reverse-padlock Amplicon Encoding FISH (RAEFISH) with whole-genome level coverage while retaining single-molecule spatial resolution in intact tissues. We demonstrate image-based spatial transcriptomics targeting 23,000 human transcript species or 22,000 mouse transcript species, including nearly the entire protein-coding transcriptome and several thousand long-noncoding RNAs, in single cells in cultures and in tissue sections. Our analyses reveal differential subcellular localizations of diverse transcripts, cell-type-specific and cell-type-invariant tissue zonation dependent transcriptome, and gene expression programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.

Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics

MOTIVATION

The rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets.

RESULTS

In response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of 30 synthesized and 74 real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists in selecting suitable algorithms for their research, while also aid bioinformatics scientists in developing more powerful and efficient computational methods in spatial transcriptomic research.

AVAILABILITY AND IMPLEMENTATION

The source code of this benchmarking framework is available at both Github (https://github.com/XiDsLab/svg-benchmark) and Zenodo (https://doi.org/10.5281/zenodo.15031083). In addition, all real and synthetic datasets considered in this study are also publicly available at Zenodo (https://doi.org/10.5281/zenodo.7227771).

Spatial relationships and interactions of immune cell niches are linked to the pathologic response of muscle-invasive bladder cancer to neoadjuvant therapy

BACKGROUND

The identification of the complex spatial architecture of immune cell infiltration and its interaction mechanisms within tumor ecosystems provides crucial insights into therapeutic responses to neoadjuvant therapy in muscle-invasive bladder cancer (MIBC). This study aims to characterize the spatial features of distinct cell-type niches within the tumor microenvironment (TME) of patients with varying responses to neoadjuvant therapy.

METHODS

We performed spatial transcriptomic profiling on six MIBC specimens obtained from a registered clinical trial (ChiCTR2000032359), generating whole-transcriptome spatial atlases to map the TME architecture. High-throughput analytical frameworks were employed to deconstruct the TME, and key findings were validated through immunohistochemistry and mouse model experiments.

RESULTS

Our analysis revealed that tissues from complete responders exhibited greater infiltration of T and B cells, with the formation of tertiary lymphoid structure (TLS). Trajectory analysis identified CCL19/CCL21 as the key signaling molecules driving TLS formation in MIBC. Mouse experiments demonstrated that recombinant CCL19/CCL21 protein injections promoted intratumoral TLS formation and enhance the efficacy of immunotherapy. Furthermore, we observed significant intrinsic heterogeneity within individual tumors, which may contribute to the lack of therapeutic efficacy in MIBC.

CONCLUSIONS

This study underscores the critical role of TLS formation in the response to neoadjuvant therapy in MIBC. We identified CCL19/CCL21 as key drivers of TLS formation within MIBC tumors and potential immune-sensitizing agents. Additionally, the intrinsic heterogeneity of tumor should be considered a significant factor influencing therapeutic efficacy.

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data

Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.

Spatially resolved genome-wide joint profiling of epigenome and transcriptome with spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq

The epigenome of a cell is tightly correlated with gene transcription, which controls cell identity and diverse biological activities. Recent advances in spatial technologies have improved our understanding of tissue heterogeneity by analyzing transcriptomics or epigenomics with spatial information preserved, but have been mainly restricted to one molecular layer at a time. Here we present procedures for two spatially resolved sequencing methods, spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq, that co-profile transcriptome and epigenome genome wide. In both methods, transcriptomic readouts are generated through tissue fixation, permeabilization and in situ reverse transcription. In spatial-ATAC-RNA-seq, Tn5 transposase is used to probe accessible chromatin, and in spatial-CUT&Tag-RNA-seq, the tissue is incubated with primary antibodies that target histone modifications, followed by Protein A-fused Tn5-induced tagmentation. Both methods leverage a microfluidic device that delivers two sets of oligonucleotide barcodes to generate a two-dimensional mosaic of tissue pixels at near single-cell resolution. A spatial-ATAC-RNA-seq or spatial-CUT&Tag-RNA-seq library can be generated in 3-5 d, allowing researchers to simultaneously investigate the transcriptomic landscape and epigenomic landscape of an intact tissue section. This protocol is an extension of our previous spatially resolved epigenome sequencing protocol and provides opportunities in multimodal profiling.

CellPie: a scalable spatial transcriptomics factor discovery method via joint non-negative matrix factorization

Spatially resolved transcriptomics has enabled the study of expression of genes within tissues while retaining their spatial identity. Most spatial transcriptomics (ST) technologies generate a matched histopathological image as part of the standard pipeline, providing morphological information that can complement the transcriptomics data. Here, we present CellPie, a fast, unsupervised factor discovery method based on joint non-negative matrix factorization of spatial RNA transcripts and histological image features. CellPie employs the accelerated hierarchical least squares method to significantly reduce the computational time, enabling efficient application to high-dimensional ST datasets. We assessed CellPie on three different human cancer types with different spatial resolutions, including a highly resolved Visium HD dataset, demonstrating both good performance and high computational efficiency compared to existing methods.

Fibrosis: cross-organ biology and pathways to development of innovative drugs

Fibrosis is a pathophysiological mechanism involved in chronic and progressive diseases that results in excessive tissue scarring. Diseases associated with fibrosis include metabolic dysfunction-associated steatohepatitis (MASH), inflammatory bowel diseases (IBDs), chronic kidney disease (CKD), idiopathic pulmonary fibrosis (IPF) and systemic sclerosis (SSc), which are collectively responsible for substantial morbidity and mortality. Although a few drugs with direct antifibrotic activity are approved for pulmonary fibrosis and considerable progress has been made in the understanding of mechanisms of fibrosis, translation of this knowledge into effective therapies continues to be limited and challenging. With the aim of assisting developers of novel antifibrotic drugs, this Review integrates viewpoints of biologists and physician-scientists on core pathways involved in fibrosis across organs, as well as on specific characteristics and approaches to assess therapeutic interventions for fibrotic diseases of the lung, gut, kidney, skin and liver. This discussion is used as a basis to propose strategies to improve the translation of potential antifibrotic therapies.

Acquisition and transcriptomic analysis of tissue micro-regions using a capillary-based method

Profiling the site-specific transcriptomes of microregions of interest (mROIs) contributes to a complete understanding of multicellular organisms. However, the simple and efficient isolation of mROIs for spatially detecting gene expression remains challenging. Here, we develop an efficient capillary-based microdissection system (CMS) for precisely isolating targeted samples from tissue sections. Optimized sampling procedures reveal that CMS can perform mROI isolation with an efficiency of 97.9 %, and detect a sufficient number of genes for gene expression profiling (CMS-seq). We apply CMS-seq to uncover spatial heterogeneity in the cortex region of the mouse, and the subregions of hippocampus in an Alzheimer's disease (AD) mouse. Results demonstrate that CMS-seq can profile spatial transcriptomes in tissue sections and holds promise for application spatial multi-omics.

MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning

Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions in undissociated tissues. Integrative analysis of multi-modal SRT data holds immense potential for understanding biological mechanisms. Here, we present a flexible multi-modal contrastive learning for the integration of SRT data (MuCST), which joins denoising, heterogeneity elimination, and compatible feature learning. MuCST accurately identifies spatial domains and is applicable to diverse datasets platforms. Overall, MuCST provides an alternative for integrative analysis of multi-modal SRT data ( https://github.com/xkmaxidian/MuCST ).

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective

The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. Despite this progress, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, and are often contaminated by noise and uncertainty, obscuring the underlying biological signal. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, metabolite levels, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering approaches struggle with the complexity of biological networks, while deep learning, with its ability to handle high-dimensional data and automatically identify meaningful patterns, has shown great promise in overcoming these challenges. Besides systematically reviewing the strengths and weaknesses of advanced deep learning methods, we have curated 21 datasets from nine benchmarks to evaluate the performance of 58 computational methods. Our analysis reveals that model performance can vary significantly across different benchmark datasets and evaluation metrics, providing a useful perspective for selecting the most appropriate approach based on a specific application scenario. We highlight three key areas for future development, offering valuable insights into how deep learning can be effectively applied to transcriptomic data analysis in biological, medical, and clinical settings.

STForte: tissue context-specific encoding and consistency-aware spatial imputation for spatially resolved transcriptomics

Encoding spatially resolved transcriptomics (SRT) data serves to identify the biological semantics of RNA expression within the tissue while preserving spatial characteristics. Depending on the analytical scenario, one may focus on different contextual structures of tissues. For instance, anatomical regions reveal consistent patterns by focusing on spatial homogeneity, while elucidating complex tumor micro-environments requires more expression heterogeneity. However, current spatial encoding methods lack consideration of the tissue context. Meanwhile, most developed SRT technologies are still limited in providing exact patterns of intact tissues due to limitations such as low resolution or missed measurements. Here, we propose STForte, a novel pairwise graph autoencoder-based approach with cross-reconstruction and adversarial distribution matching, to model the spatial homogeneity and expression heterogeneity of SRT data. STForte extracts interpretable latent encodings, enabling downstream analysis by accurately portraying various tissue contexts. Moreover, STForte allows spatial imputation using only spatial consistency to restore the biological patterns of unobserved locations or low-quality cells, thereby providing fine-grained views to enhance the SRT analysis. Extensive evaluations of datasets under different scenarios and SRT platforms demonstrate that STForte is a scalable and versatile tool for providing enhanced insights into spatial data analysis.

STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes

Here we present STModule, a Bayesian method developed to identify tissue modules from spatially resolved transcriptomics that reveal spatial components and essential characteristics of tissues. STModule uncovers diverse expression signals in transcriptomic landscapes such as cancer, intraepithelial neoplasia, immune infiltration, outcome-related molecular features and various cell types, which facilitate downstream analysis and provide insights into tumor microenvironments, disease mechanisms, treatment development, and histological organization of tissues. STModule captures a broader spectrum of biological signals compared to other methods and detects novel spatial components. The tissue modules characterized by gene sets demonstrate greater robustness and transferability across different biopsies. STModule: https://github.com/rwang-z/STModule.git .

Single-cell technology grows up: Leveraging high-resolution omics approaches to understand neurodevelopmental disorders

The identification of hundreds of neurodevelopmental disorder (NDD) genes in the last decade led to numerous genetic models for understanding NDD gene mutation consequences and delineating putative neurobiological mediators of disease. In parallel, single-cell and single-nucleus genomic technologies have been developed and implemented to create high-resolution atlases of cell composition, gene expression, and circuit connectivity in the brain. Here, we discuss the opportunities to leverage mutant models (or human tissue, where available) and genomics approaches to systematically define NDD etiology at cellular resolution. We review progress in applying single-cell and spatial transcriptomics to interrogate developmental trajectories, cellular composition, circuit activity, and connectivity across human tissue and NDD models. We discuss considerations for implementing these approaches at scale to maximize insights and facilitate reproducibility. Finally, we highlight how standardized application of these technologies promises to not only define etiologies of individual disorders but also identify molecular, cellular, and circuit level convergence across NDDs.

Decoding the blueprints of embryo development with single-cell and spatial omics

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases?

Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.

Spatio-temporal transcriptomic analysis reveals distinct nephrotoxicity, DNA damage, and regeneration response after cisplatin

Nephrotoxicity caused by drug or chemical exposure involves complex mechanisms as well as a temporal integration of injury and repair responses in different nephron segments. Distinct cellular transcriptional programs regulate the time-dependent tissue injury and regeneration responses. Whole kidney transcriptome analysis cannot dissect the complex spatio-temporal injury and regeneration responses in the different nephron segments. Here, we used laser capture microdissection of formalin-fixed paraffin embedded sections followed by whole genome targeted RNA-sequencing-TempO-Seq and co-expression gene-network (module) analysis to determine the spatial-temporal responses in rat kidney glomeruli (GM), cortical proximal tubules (CPT) and outer-medulla proximal tubules (OMPT) comparison with whole kidney, after a single dose of the nephrotoxicant cisplatin. We demonstrate that cisplatin induced early onset of DNA damage in both CPT and OMPT, but not GM. Sustained DNA damage response was strongest in OMPT coinciding with OMPT specific inflammatory signaling, actin cytoskeletal remodeling and increased glycolytic metabolism with suppression of mitochondrial activity. Later responses reflected regeneration-related cell cycle pathway activation and ribosomal biogenesis in the injured OMPT regions. Activation of modules containing kidney injury biomarkers was strongest in OMPT, with OMPT Clu expression highly correlating with urinary clusterin biomarker measurements compared the correlation of Kim1. Our findings also showed that whole kidney responses were less sensitive than OMPT. In conclusion, our LCM-TempO-Seq method reveals a detailed spatial mechanistic understanding of renal injury/regeneration after nephrotoxicant exposure and identifies the most representative mechanism-based nephron segment specific renal injury biomarkers.

Spatial transcriptomics in autoimmune rheumatic disease: potential clinical applications and perspectives

Spatial transcriptomics is a cutting-edge technology that analyzes gene expression at the cellular level within tissues while integrating spatial location information. This concept, which combines high-plex RNA sequencing with spatial data, emerged in the early 2010s. Spatial transcriptomics has rapidly expanded with the development of technologies such as in situ hybridization, in situ sequencing, in situ spatial barcoding, and microdissection-based methods. Each technique offers advanced mapping resolution and precise spatial assessments at the single-cell level. Over the past decade, the use of spatial transcriptomics on clinical samples has enabled researchers to identify gene expressions in specific diseased foci, significantly enhancing our understanding of cellular interactions and disease processes. In the field of rheumatology, the complex and elusive pathophysiology of diseases such as rheumatoid arthritis, systemic lupus erythematosus, and Sjögren's syndrome remains a challenge for personalized treatment. Spatial transcriptomics provides insights into how different cell populations interact within disease foci, such as the synovial tissue, kidneys, and salivary glands. This review summarizes the development of spatial transcriptomics and current insights into the pathophysiology of autoimmune rheumatic diseases, focusing on immune cell distribution and cellular interactions within tissues. We also explore the potential of spatial transcriptomics from a clinical perspective and discuss the possibilities for translating this technology to the bedside.

SpaceBar enables clone tracing in spatial transcriptomic data

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

Seq-Scope-eXpanded: Spatial Omics Beyond Optical Resolution

Sequencing-based spatial transcriptomics (sST) enables transcriptome-wide gene expression mapping but falls short of reaching the optical resolution (200-300 nm) of imaging-based methods. Here, we present Seq-Scope-X (Seq-Scope-eXpanded), which empowers submicrometer-resolution Seq-Scope with tissue expansion to surpass this limitation. By physically enlarging tissues, Seq-Scope-X minimizes transcript diffusion effects and increases spatial feature density by an additional order of magnitude. In liver tissue, this approach resolves nuclear and cytoplasmic compartments in nearly every single cell, uncovering widespread differences between nuclear and cytoplasmic transcriptome patterns. Independently confirmed by imaging-based methods, these results suggest that individual hepatocytes can dynamically switch their metabolic roles. Seq-Scope-X is also applicable to non-hepatic tissues such as brain and colon, and can be modified to perform spatial proteomic analysis, simultaneously profiling hundreds of barcode-tagged antibody stains at microscopic resolutions in mouse spleens and human tonsils. These findings establish Seq-Scope-X as a transformative tool for ultra-high-resolution whole-transcriptome and proteome profiling, offering unparalleled spatial precision and advancing our understanding of cellular architecture, function, and disease mechanisms.

scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data

Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in two and three-dimensional space. However, sparse sequencing capacity and growing spatial resolution in spatial omics present significant computational challenges in identifying biologically meaningful molecules that exhibit variable spatial distributions across different omics. We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in high-resolution spatial omics data. scBSP leverages sparse matrix operation to significantly increase computational efficiency in both computational time and memory usage. In diverse spatial sequencing data and simulations, scBSP consistently and rapidly identifies spatially variable genes and spatially variable peaks across various sequencing techniques and spatial resolutions, handling two- and three-dimensional data with up to millions of cells. It can process high-definition spatial transcriptomics data for 19,950 genes across 181,367 spots within 10 seconds on a typical desktop computer, making it the fastest tool available for handling such high-resolution, sparse spatial omics data while maintaining high accuracy. In a case study of kidney disease using 10x Xenium data, scBSP identified spatially variable genes representative of critical pathological mechanisms associated with histology.

Environmental community transcriptomics: strategies and struggles

Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the intention of better understanding the interactions between members of the community. Community transcriptomics requires successful extraction of RNA from a diverse set of organisms and subsequent analysis via mapping those reads to a reference genome or de novo assembly of the reads. Both, extraction protocols and the analysis steps can pose hurdles for community transcriptomics. This review covers advances in transcriptomic techniques and assesses the viability of applying them to community transcriptomics.

Applications of Nanotechnology for Spatial Omics: Biological Structures and Functions at Nanoscale Resolution

Spatial omics methods are extensions of traditional histological methods that can illuminate important biomedical mechanisms of physiology and disease by examining the distribution of biomolecules, including nucleic acids, proteins, lipids, and metabolites, at microscale resolution within tissues or individual cells. Since, for some applications, the desired resolution for spatial omics approaches the nanometer scale, classical tools have inherent limitations when applied to spatial omics analyses, and they can measure only a limited number of targets. Nanotechnology applications have been instrumental in overcoming these bottlenecks. When nanometer-level resolution is needed for spatial omics, super resolution microscopy or detection imaging techniques, such as mass spectrometer imaging, are required to generate precise spatial images of target expression. DNA nanostructures are widely used in spatial omics for purposes such as nucleic acid detection, signal amplification, and DNA barcoding for target molecule labeling, underscoring advances in spatial omics. Other properties of nanotechnologies include advanced spatial omics methods, such as microfluidic chips and DNA barcodes. In this review, we describe how nanotechnologies have been applied to the development of spatial transcriptomics, proteomics, metabolomics, epigenomics, and multiomics approaches. We focus on how nanotechnology supports improved resolution and throughput of spatial omics, surpassing traditional techniques. We also summarize future challenges and opportunities for the application of nanotechnology to spatial omics methods.

Lessons learned from spatial transcriptomic analyses in clear-cell renal cell carcinoma

Spatial transcriptomics has emerged as a powerful tool for discerning the heterogeneity of the tumour microenvironment across various cancers, including renal cell carcinoma (RCC). Spatial transcriptomics-based studies conducted in clear-cell RCC (the only RCC subtype studied using this technique to date) have given insights into spatial interactions within this disease. These insights include the role of epithelial-to-mesenchymal transitioning, revealing proximity-dependent interactions between tumour cells, fibroblasts, interleukin-2-expressing macrophages and hyalinized regions. Investigations into metabolic programmes have shown high transcriptional heterogeneity within tumours, with a tendency of increased metabolic activity towards the tumour centre. T cell infiltration has been shown to be independent of neoantigen burden, although T cell activity correlates with both metabolic states and various transcripts expressed by tumour cells, fibroblasts and monocytes. The role of tertiary lymphoid structures in both plasma cell maturation and their infiltration of the tumour has been shown through tracks of fibroblasts. Collectively, these findings indicate the potential of spatial transcriptomics to reveal predictive spatial features, supporting its promise in the development of biomarkers for clear-cell RCC management.

Exploring the cellular and molecular basis of murine cardiac development through spatiotemporal transcriptome sequencing

BACKGROUND

Spatial transcriptomics is a powerful tool that integrates molecular data with spatial information, thereby facilitating a deeper comprehension of tissue morphology and cellular interactions. In our study, we utilized cutting-edge spatial transcriptome sequencing technology to explore the development of the mouse heart and construct a comprehensive spatiotemporal cell atlas of early murine cardiac development.

RESULTS

Through the analysis of this atlas, we elucidated the spatial organization of cardiac cellular lineages and their interactions during the developmental process. Notably, we observed dynamic changes in gene expression within fibroblasts and cardiomyocytes. Moreover, we identified critical genes, such as Igf2, H19, and Tcap, as well as transcription factors Tcf12 and Plagl1, which may be associated with the loss of myocardial regeneration ability during early heart development. In addition, we successfully identified marker genes, like Adamts8 and Bmp10, that can distinguish between the left and right atria.

CONCLUSION

Our study provides novel insights into murine cardiac development and offers a valuable resource for future investigations in the field of heart research, highlighting the significance of spatial transcriptomics in understanding the complex processes of organ development.

Unveiling patterns in spatial transcriptomics data: a novel approach utilizing graph attention autoencoder and multiscale deep subspace clustering network

BACKGROUND

The accurate deciphering of spatial domains, along with the identification of differentially expressed genes and the inference of cellular trajectory based on spatial transcriptomic (ST) data, holds significant potential for enhancing our understanding of tissue organization and biological functions. However, most of spatial clustering methods can neither decipher complex structures in ST data nor entirely employ features embedded in different layers.

RESULTS

This article introduces STMSGAL, a novel framework for analyzing ST data by incorporating graph attention autoencoder and multiscale deep subspace clustering. First, STMSGAL constructs ctaSNN, a cell type-aware shared nearest neighbor graph, using Louvian clustering exclusively based on gene expression profiles. Subsequently, it integrates expression profiles and ctaSNN to generate spot latent representations using a graph attention autoencoder and multiscale deep subspace clustering. Lastly, STMSGAL implements spatial clustering, differential expression analysis, and trajectory inference, providing comprehensive capabilities for thorough data exploration and interpretation. STMSGAL was evaluated against 7 methods, including SCANPY, SEDR, CCST, DeepST, GraphST, STAGATE, and SiGra, using four 10x Genomics Visium datasets, 1 mouse visual cortex STARmap dataset, and 2 Stereo-seq mouse embryo datasets. The comparison showcased STMSGAL's remarkable performance across Davies-Bouldin, Calinski-Harabasz, S_Dbw, and ARI values. STMSGAL significantly enhanced the identification of layer structures across ST data with different spatial resolutions and accurately delineated spatial domains in 2 breast cancer tissues, adult mouse brain (FFPE), and mouse embryos.

CONCLUSIONS

STMSGAL can serve as an essential tool for bridging the analysis of cellular spatial organization and disease pathology, offering valuable insights for researchers in the field.

Trends and Challenges of the Modern Pathology Laboratory for Biopharmaceutical Research Excellence

Pathology, a fundamental discipline that bridges basic scientific discovery to the clinic, is integral to successful drug development. Intrinsically multimodal and multidimensional, anatomic pathology continues to be empowered by advancements in molecular and digital technologies enabling the spatial tissue detection of biomolecules such as genes, transcripts, and proteins. Over the past two decades, breakthroughs in spatial molecular biology technologies and advancements in automation and digitization of laboratory processes have enabled the implementation of higher throughput assays and the generation of extensive molecular data sets from tissue sections in biopharmaceutical research and development research units. It is our goal to provide readers with some rationale, advice, and ideas to help establish a modern molecular pathology laboratory to meet the emerging needs of biopharmaceutical research. This manuscript provides (1) a high-level overview of the current state and future vision for excellence in research pathology practice and (2) shared perspectives on how to optimally leverage the expertise of discovery, toxicologic, and translational pathologists to provide effective spatial, molecular, and digital pathology data to support modern drug discovery. It captures insights from the experiences, challenges, and solutions from pathology laboratories of various biopharmaceutical organizations, including their approaches to troubleshooting and adopting new technologies.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Spatial-Omics Methods and Applications

Traditional tissue profiling approaches have evolved from bulk studies to single-cell analysis over the last decade; however, the spatial context in tissues and microenvironments has always been lost. Over the last 5 years, spatial technologies have emerged that enabled researchers to investigate tissues in situ for proteins and transcripts without losing anatomy and histology. The field of spatial-omics enables highly multiplexed analysis of biomolecules like RNAs and proteins in their native spatial context-and has matured from initial proof-of-concept studies to a thriving field with widespread applications from basic research to translational and clinical studies. While there has been wide adoption of spatial technologies, there remain challenges with the standardization of methodologies, sample compatibility, throughput, resolution, and ease of use. In this chapter, we discuss the current state of the field and highlight technological advances and limitations.

Histopathologic Analysis of Human Kidney Spatial Transcriptomics Data: Toward Precision Pathology

The application of spatial transcriptomics (ST) technologies is booming and has already yielded important insights across many different tissues and disease models. In nephrology, ST technologies have helped to decipher the cellular and molecular mechanisms in kidney diseases and have allowed the recent creation of spatially anchored human kidney atlases of healthy and diseased kidney tissues. During ST data analysis, the computationally annotated clusters are often superimposed on a histologic image without their initial identification being based on the morphologic and/or spatial analyses of the tissues and lesions. Herein, histopathologic ST data from a human kidney sample were modeled to correspond as closely as possible to the kidney biopsy sample in a health care or research context. This study shows the feasibility of a morphology-based approach to interpreting ST data, helping to improve our understanding of the lesion phenomena at work in chronic kidney disease at both the cellular and the molecular level. Finally, the newly identified pathology-based clusters could be accurately projected onto other slides from nephrectomy or needle biopsy samples. Thus, they serve as a reference for analyzing other kidney tissues, paving the way for the future of molecular microscopy and precision pathology.