SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

Cell-cell communication is a vital feature involving numerous biological processes. Here, the authors develop SpaTalk, a cell-cell communication inference method using knowledge graph for spatially resolved transcriptomic data, providing valuable insights into spatial intercellular tissue dynamics.

The emerging landscape of spatial profiling technologies

Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks

The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations of cells or spots, and the corresponding hematoxylin and eosin-stained histology images. It turns promising to predict gene expression from histology images that are relatively easy and cheap to obtain. For this purpose, several methods are devised, but they have not fully captured the internal relations of the 2D vision features or spatial dependency between spots. Here, we developed Hist2ST, a deep learning-based model to predict RNA-seq expression from histology images. Around each sequenced spot, the corresponding histology image is cropped into an image patch and fed into a convolutional module to extract 2D vision features. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. By comprehensive tests on cancer and normal datasets, Hist2ST was shown to outperform existing methods in terms of both gene expression prediction and spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.

SPROUT: spectral sparsification helps restore the spatial structure at single-cell resolution

Single-cell RNA sequencing thoroughly quantifies the individual cell transcriptomes but renounces the spatial structure. Conversely, recently emerged spatial transcriptomics technologies capture the cellular spatial structure but skimp cell or gene resolutions. Ligand-receptor interactions reveal the potential of cell proximity since they are spatially constrained. Cell–cell affinity values estimated by ligand–receptor interaction can partially represent the structure of cells but falsely include the pseudo affinities between distant or indirectly interacting cells. Here, we develop a software package, SPROUT, to reconstruct the single-cell resolution spatial structure from the transcriptomics data through diminished pseudo ligand–receptor affinities. For spatial data, SPROUT first curates the representative single-cell profiles for each spatial spot from a candidate library, then reduces the pseudo affinities in the intercellular affinity matrix by partial correlation, spectral graph sparsification, and spatial coordinates refinement. SPROUT embeds the estimated interactions into a low-dimensional space with the cross-entropy objective to restore the intercellular structures, which facilitates the discovery of dominant ligand–receptor pairs between neighboring cells at single-cell resolution. SPROUT reconstructed structures achieved shape Pearson correlations ranging from 0.91 to 0.97 on the mouse hippocampus and human organ tumor microenvironment datasets. Furthermore, SPROUT can solely de novo reconstruct the structures at single-cell resolution, i.e., reaching the cell-type proximity correlations of 0.68 and 0.89 between reconstructed and immunohistochemistry-informed spatial structures on a human developing heart dataset and a tumor microenvironment dataset, respectively.

Lymphatics act as a signaling hub to regulate intestinal stem cell activity

Barrier epithelia depend upon resident stem cells for homeostasis, defense, and repair. Epithelial stem cells of small and large intestines (ISCs) respond to their local microenvironments (niches) to fulfill a continuous demand for tissue turnover. The complexity of these niches and underlying communication pathways are not fully known. Here, we report a lymphatic network at the intestinal crypt base that intimately associates with ISCs. Employing in vivo loss of function and lymphatic:organoid cocultures, we show that crypt lymphatics maintain ISCs and inhibit their precocious differentiation. Pairing single-cell and spatial transcriptomics, we apply BayesPrism to deconvolve expression within spatial features and develop SpaceFold to robustly map the niche at high resolution, exposing lymphatics as a central signaling hub for the crypt in general and ISCs in particular. We identify WNT-signaling factors (WNT2, R-SPONDIN-3) and a hitherto unappreciated extracellular matrix protein, REELIN, as crypt lymphatic signals that directly govern the regenerative potential of ISCs.

Identification of cell-type-specific spatially variable genes accounting for excess zeros

MOTIVATION

Spatial transcriptomic techniques can profile gene expressions while retaining the spatial information, thus offering unprecedented opportunities to explore the relationship between gene expression and spatial locations. The spatial relationship may vary across cell types, but there is a lack of statistical methods to identify cell-type-specific spatially variable (SV) genes by simultaneously modeling excess zeros and cell-type proportions.

RESULTS

We develop a statistical approach CTSV to detect cell-type-specific SV genes. CTSV directly models spatial raw count data and considers zero-inflation as well as overdispersion using a zero-inflated negative binomial distribution. It then incorporates cell-type proportions and spatial effect functions in the zero-inflated negative binomial regression framework. The R package pscl is employed to fit the model. For robustness, a Cauchy combination rule is applied to integrate P-values from multiple choices of spatial effect functions. Simulation studies show that CTSV not only outperforms competing methods at the aggregated level but also achieves more power at the cell-type level. By analyzing pancreatic ductal adenocarcinoma spatial transcriptomic data, SV genes identified by CTSV reveal biological insights at the cell-type level.

AVAILABILITY AND IMPLEMENTATION

The R package of CTSV is available at https://bioconductor.org/packages/devel/bioc/html/CTSV.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

Identification of early neurodegenerative pathways in progressive multiple sclerosis

Progressive multiple sclerosis (MS) is characterized by unrelenting neurodegeneration, which causes cumulative disability and is refractory to current treatments. Drug development to prevent disease progression is an urgent clinical need yet is constrained by an incomplete understanding of its complex pathogenesis. Using spatial transcriptomics and proteomics on fresh-frozen human MS brain tissue, we identified multicellular mechanisms of progressive MS pathogenesis and traced their origin in relation to spatially distributed stages of neurodegeneration. By resolving ligand-receptor interactions in local microenvironments, we discovered defunct trophic and anti-inflammatory intercellular communications within areas of early neuronal decline. Proteins associated with neuronal damage in patient samples showed mechanistic concordance with published in vivo knockdown and central nervous system (CNS) disease models, supporting their causal role and value as potential therapeutic targets in progressive MS. Our findings provide a new framework for drug development strategies, rooted in an understanding of the complex cellular and signaling dynamics in human diseased tissue that facilitate this debilitating disease.

Spatially resolved transcriptomics and the kidney: Many opportunities

Defining changes in gene expression during health and disease is critical for the understanding of human physiology. In recent years, single cell/nuclei RNA sequencing (sc/snRNAseq) has revolutionized the definition and discovery of cell types and states, as well as the interpretation of organ and cell type specific signaling pathways. However, these advances require tissue dissociation to the level of the single cell or single nuclei level. Spatially resolved transcriptomics (SrT) now provides a platform to overcome this barrier in understanding the physiological contexts of gene expression and cellular microenvironment changes in development and disease. Some of these transcriptomic tools allow for high resolution mapping of hundreds of genes simultaneously in cellular and subcellular compartments. Other tools offer genome depth mapping, but at lower resolution. Here, we will review advances in SrT, considerations for using SrT in your own research, and applications for kidney biology.

Single-cell sequencing: expansion, integration and translation

With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.

A comprehensive comparison on cell-type composition inference for spatial transcriptomics data

Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 methods, with either ideal reference or non-ideal reference. Furthermore, we examine the performance of these methods on spot- and bead-level ST data by comparing estimated cell-type proportions to carefully matched single-cell ST data. In comparing the performance on various tissues and technological platforms, we concluded that RCTD and stereoscope achieve more robust and accurate inferences.

An introduction to spatial transcriptomics for biomedical research

Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as 'spatial transcriptomics'. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.

Molecular signatures of long-term hepatocellular carcinoma risk in nonalcoholic fatty liver disease

Prediction of hepatocellular carcinoma (HCC) risk is an urgent unmet need in patients with nonalcoholic fatty liver disease (NAFLD). In cohorts of 409 patients with NAFLD from multiple global regions, we defined and validated hepatic transcriptome and serum secretome signatures predictive of long-term HCC risk in patients with NAFLD. A 133-gene signature, prognostic liver signature (PLS)-NAFLD, predicted incident HCC over up to 15 years of longitudinal observation. High-risk PLS-NAFLD was associated with IDO1+ dendritic cells and dysfunctional CD8+ T cells in fibrotic portal tracts along with impaired metabolic regulators. PLS-NAFLD was validated in independent cohorts of patients with NAFLD who were HCC naïve (HCC incidence rates at 15 years were 22.7 and 0% in high- and low-risk patients, respectively) or HCC experienced (de novo HCC recurrence rates at 5 years were 71.8 and 42.9% in high- and low-risk patients, respectively). PLS-NAFLD was bioinformatically translated into a four-protein secretome signature, PLSec-NAFLD, which was validated in an independent cohort of HCC-naïve patients with NAFLD and cirrhosis (HCC incidence rates at 15 years were 37.6 and 0% in high- and low-risk patients, respectively). Combination of PLSec-NAFLD with our previously defined etiology-agnostic PLSec-AFP yielded improved HCC risk stratification. PLS-NAFLD was modified by bariatric surgery, lipophilic statin, and IDO1 inhibitor, suggesting that the signature can be used for drug discovery and as a surrogate end point in HCC chemoprevention clinical trials. Collectively, PLS/PLSec-NAFLD may enable NAFLD-specific HCC risk prediction and facilitate clinical translation of NAFLD-directed HCC chemoprevention.

Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to analyze the expression level of tissues at a cellular resolution. However, it could not capture the spatial organization of cells in a tissue. The spatially resolved transcriptomics technologies (ST) have been developed to address this issue. However, the emerging STs are still inefficient at single-cell resolution and/or fail to capture the sufficient reads. To this end, we adopted a partial least squares-based method (spatial modular patterns [SpaMOD]) to simultaneously integrate the two data modalities, as well as the networks related to cells and spots, to identify the cell-spot comodules for deciphering the SpaMOD of tissues. We applied SpaMOD to three paired scRNA-seq and ST datasets, derived from the mouse brain, granuloma, and pancreatic ductal adenocarcinoma, respectively. The identified cell-spot comodules provide detailed biological insights into the spatial relationships between cell populations and their spatial locations in the tissue.

Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma

Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies. The source of dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas by spatially resolved transcriptomics, metabolomics, and proteomics. By deciphering regionally shared transcriptional programs across patients, we infer that glioblastoma is organized by spatial segregation of lineage states and adapts to inflammatory and/or metabolic stimuli, reminiscent of the reactive transformation in mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. A model of glioblastoma stem cells implanted into human and rodent neocortical tissue mimicking various environments confirmed that transcriptional states originate from dynamic adaptation to various environments.

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods’ predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.

Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches.

Integrating spatial transcriptomics with single-cell transcriptomics reveals a spatiotemporal gene landscape of the human developing kidney

BACKGROUND

Research on spatiotemporal gene landscape can provide insights into the spatial characteristics of human kidney development and facilitate kidney organoid cultivation. Here, we profiled the spatiotemporal gene programs of the human embryonic kidneys at 9 and 18 post-conception weeks (PCW) by integrating the application of microarray-based spatial transcriptomics and single-cell transcriptomics.

RESULTS

We mapped transcriptomic signatures of scRNA-seq cell types upon the 9 and 18 PCW kidney sections based on cell-type deconvolution and multimodal intersection analyses, depicting a spatial landscape of developing cell subpopulations. We established the gene characteristics in the medullary regions and revealed a strong mitochondrial oxidative phosphorylation and glycolysis activity in the deeper medullary region. We also built a regulatory network centered on GDNF-ETV4 for nephrogenic niche development based on the weighted gene co-expression network analysis and highlighted the key roles of Wnt, FGF, and JAG1-Notch2 signaling in maintaining renal branching morphogenesis.

CONCLUSIONS

Our findings obtained by this spatiotemporal gene program are expected to improve the current understanding of kidney development.

Single cell- and spatial 'Omics revolutionize physiology

Single cell multi- 'Omics and Spatial Transcriptomics are prominent technological highlights of recent years, and both fields still witness a ceaseless firework of novel approaches for high resolution profiling of additional omics layers. As all life processes in organs and organisms are based on the functions of their fundamental building blocks, the individual cells and their interactions, these methods are of utmost worth for the study of physiology in health and disease. Recent discoveries on embryonic development, tumor immunology, the detailed cellular composition and function of complex tissues like for example the kidney or the brain, different roles of the same cell type in different organs, the oncogenic program of individual tumor entities, or the architecture of immunopathology in infected tissue are based on single cell and spatial transcriptomics experiments. In this review, we will give a broad overview of technological concepts for single cell and spatial analysis, showing both advantages and limitations, and illustrate their impact with some particularly impressive case studies.

Emerging Artificial Intelligence Applications in Spatial Transcriptomics Analysis

Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.

SpotClean adjusts for spot swapping in spatial transcriptomics data

Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies.

Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

Spatial transcriptomics approaches have substantially advanced our capacity to detect the spatial distribution of RNA transcripts in tissues, yet it remains challenging to characterize whole-transcriptome-level data for single cells in space. Addressing this need, researchers have developed integration methods to combine spatial transcriptomic data with single-cell RNA-seq data to predict the spatial distribution of undetected transcripts and/or perform cell type deconvolution of spots in histological sections. However, to date, no independent studies have comparatively analyzed these integration methods to benchmark their performance. Here we present benchmarking of 16 integration methods using 45 paired datasets (comprising both spatial transcriptomics and scRNA-seq data) and 32 simulated datasets. We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots. We provide a benchmark pipeline to help researchers select optimal integration methods to process their datasets.

Spatially informed cell-type deconvolution for spatial transcriptomics

Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive deconvolution (CARD), that combines cell type–specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell type composition across tissue locations. Modeling spatial correlation allows us to borrow the cell-type composition information across locations, improving accuracy of deconvolution even with a mismatched scRNA-seq reference. CARD can also impute cell type compositions and gene expression levels at unmeasured tissue locations, enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study, and perform deconvolution without a scRNA-seq reference. Applications to four datasets including a pancreatic cancer dataset identified multiple cell types and molecular markers with distinct spatial localization that define the progression, heterogeneity, and compartmentalization of pancreatic cancer.

Alignment and integration of spatial transcriptomics data

Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information.

Digital spatial profiling of collapsing glomerulopathy

Collapsing glomerulopathy is a histologically distinct variant of focal and segmental glomerulosclerosis that presents with heavy proteinuria and portends a poor prognosis. Collapsing glomerulopathy can be triggered by viral infections such as HIV or SARS-CoV-2. Transcriptional profiling of collapsing glomerulopathy lesions is difficult since only a few glomeruli may exhibit this histology within a kidney biopsy and the mechanisms driving this heterogeneity are unknown. Therefore, we used recently developed digital spatial profiling (DSP) technology which permits quantification of mRNA at the level of individual glomeruli. Using DSP, we profiled 1,852 transcripts in glomeruli isolated from formalin fixed paraffin embedded sections from HIV or SARS-CoV-2-infected patients with biopsy-confirmed collapsing glomerulopathy and used normal biopsy sections as controls. Even though glomeruli with collapsing features appeared histologically similar across both groups of patients by light microscopy, the increased resolution of DSP uncovered intra- and inter-patient heterogeneity in glomerular transcriptional profiles that were missed in early laser capture microdissection studies of pooled glomeruli. Focused validation using immunohistochemistry and RNA in situ hybridization showed good concordance with DSP results. Thus, DSP represents a powerful method to dissect transcriptional programs of pathologically discernible kidney lesions.

Spatial transcriptomics and the kidney

PURPOSE OF REVIEW

The application of spatial transcriptomics technologies to the interrogation of kidney tissue is a burgeoning effort. These technologies share a common purpose in mapping both the expression of individual molecules and entire transcriptomic signatures of kidney cell types and structures. Such information is often superimposed upon a histologic image. The resulting datasets are readily merged with other imaging and transcriptomic techniques to establish a spatially anchored atlas of the kidney. This review provides an overview of the various spatial transcriptomic technologies and recent studies in kidney disease. Potential applications gleaned from the interrogation of other organ systems, but relative to the kidney, are also discussed.

RECENT FINDINGS

Spatial transcriptomic technologies have enabled localization of whole transcriptome mRNA expression, correlation of mRNA to histology, measurement of in situ changes in expression across time, and even subcellular localization of transcripts within the kidney. These innovations continue to aid in the development of human cellular atlases of the kidney, the reclassification of disease, and the identification of important therapeutic targets.

SUMMARY

Spatial localization of gene expression will complement our current understanding of disease derived from single cell RNA sequencing, histopathology, protein immunofluorescence, and electron microscopy. Although spatial technologies continue to evolve rapidly, their importance in the localization of disease signatures is already apparent. Further efforts are required to integrate whole transcriptome and subcellular expression signatures into the individualized assessment of human kidney disease.

Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve.

Identifying cell-type-specific spatial patterns in ST data is critical for understanding tissue organization but current methods rely on external references. Here the authors develop a reference-free method to effectively recover cell-type transcriptional profiles and proportions.

Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis

A major challenge in understanding vertebrate embryogenesis is the lack of topographical transcriptomic information that can help correlate microenvironmental cues within the hierarchy of cell-fate decisions. Here, we employed Stereo-seq to profile 91 zebrafish embryo sections covering six critical time points during the first 24 h of development, obtaining a total of 152,977 spots at a resolution of 10 × 10 × 15 μm³ (close to cellular size) with spatial coordinates. Meanwhile, we identified spatial modules and co-varying genes for specific tissue organizations. By performing the integrated analysis of the Stereo-seq and scRNA-seq data from each time point, we reconstructed the spatially resolved developmental trajectories of cell-fate transitions and molecular changes during zebrafish embryogenesis. We further investigated the spatial distribution of ligand-receptor pairs and identified potentially important interactions during zebrafish embryo development. Our study constitutes a fundamental reference for further studies aiming to understand vertebrate development.

DestVI identifies continuums of cell types in spatial transcriptomics data

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).

Technique integration of single-cell RNA sequencing with spatially resolved transcriptomics in the tumor microenvironment

Background

The tumor microenvironment contributes to tumor initiation, growth, invasion, and metastasis. The tumor microenvironment is heterogeneous in cellular and acellular components, particularly structural features and their gene expression at the inter-and intra-tumor levels.

Main text

Single-cell RNA sequencing profiles single-cell transcriptomes to reveal cell proportions and trajectories while spatial information is lacking. Spatially resolved transcriptomics redeems this lack with limited coverage or depth of transcripts. Hence, the integration of single-cell RNA sequencing and spatial data makes the best use of their strengths, having insights into exploring diverse tissue architectures and interactions in a complicated network. We review applications of integrating the two methods, especially in cellular components in the tumor microenvironment, showing each role in cancer initiation and progression, which provides clinical relevance in prognosis, optimal treatment, and potential therapeutic targets.

Conclusion

The integration of two approaches may break the bottlenecks in the spatial resolution of neighboring cell subpopulations in cancer, and help to describe the signaling circuitry about the intercommunication and its exact mechanisms in producing different types and malignant stages of tumors.

Clinical and translational values of spatial transcriptomics

The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.

Super-resolved spatial transcriptomics by deep data fusion

Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone.

Spatial transcriptomic analysis reveals inflammatory foci defined by senescent cells in the white matter, hippocampi and cortical grey matter in the aged mouse brain

There is strong evidence that aging is associated with an increased presence of senescent cells in the brain. The finding that treatment with senolytic drugs improves cognitive performance of aged laboratory mice suggests that increased cellular senescence is causally linked to age-related cognitive decline. The relationship between senescent cells and their relative locations within the brain is critical to understanding the pathology of age-related cognitive decline and dementia. To assess spatial distribution of cellular senescence in the aged mouse brain, spatially resolved whole transcriptome mRNA expression was analyzed in sections of brains derived from young (3 months old) and aged (28 months old) C57BL/6 mice while capturing histological information in the same tissue section. Using this spatial transcriptomics (ST)-based method, microdomains containing senescent cells were identified on the basis of their senescence-related gene expression profiles (i.e., expression of the senescence marker cyclin-dependent kinase inhibitor p16INK4A encoded by the Cdkn2a gene) and were mapped to different anatomical brain regions. We confirmed that brain aging is associated with increased cellular senescence in the white matter, the hippocampi and the cortical grey matter. Transcriptional analysis of the senescent cell-containing ST spots shows that presence of senescent cells is associated with a gene expression signature suggestive of neuroinflammation. GO enrichment analysis of differentially expressed genes in the outer region of senescent cell-containing ST spots ("neighboring ST spots") also identified functions related to microglia activation and neuroinflammation. In conclusion, senescent cells accumulate with age in the white matter, the hippocampi and cortical grey matter and likely contribute to the genesis of inflammatory foci in a paracrine manner.

Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we develop a graph attention auto-encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validate STAGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STAGATE could be extended to multiple consecutive sections to reduce batch effects between sections and extracting three-dimensional (3D) expression domains from the reconstructed 3D tissue effectively.

Statistical and machine learning methods for spatially resolved transcriptomics data analysis

The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead.

Learning deep features and topological structure of cells for clustering of scRNA-sequencing data

Single-cell RNA sequencing (scRNA-seq) measures gene transcriptome at the cell level, paving the way for the identification of cell subpopulations. Although deep learning has been successfully applied to scRNA-seq data, these algorithms are criticized for the undesirable performance and interpretability of patterns because of the noises, high-dimensionality and extraordinary sparsity of scRNA-seq data. To address these issues, a novel deep learning subspace clustering algorithm (aka scGDC) for cell types in scRNA-seq data is proposed, which simultaneously learns the deep features and topological structure of cells. Specifically, scGDC extends auto-encoder by introducing a self-representation layer to extract deep features of cells, and learns affinity graph of cells, which provide a better and more comprehensive strategy to characterize structure of cell types. To address heterogeneity of scRNA-seq data, scGDC projects cells of various types onto different subspaces, where types, particularly rare cell types, are well discriminated by utilizing generative adversarial learning. Furthermore, scGDC joins deep feature extraction, structural learning and cell type discovery, where features of cells are extracted under the guidance of cell types, thereby improving performance of algorithms. A total of 15 scRNA-seq datasets from various tissues and organisms with the number of cells ranging from 56 to 63 103 are adopted to validate performance of algorithms, and experimental results demonstrate that scGDC significantly outperforms 14 state-of-the-art methods in terms of various measurements (on average 25.51% by improvement), where (rare) cell types are significantly associated with topology of affinity graph of cells. The proposed model and algorithm provide an effective strategy for the analysis of scRNA-seq data (The software is coded using python, and is freely available for academic https://github.com/xkmaxidian/scGDC).

Deciphering tissue structure and function using spatial transcriptomics

The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development.

Museum of spatial transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .

STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing

The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrated STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell types to spatial locations but also improved the identification of spatially localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/wanglabtongji/STRIDE.

Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images

MOTIVATION

Multiplexed imaging is a nascent single-cell assay with a complex data structure susceptible to technical variability that disrupts inference. These in situ methods are valuable in understanding cell-cell interactions, but few standardized processing steps or normalization techniques of multiplexed imaging data are available.

RESULTS

We implement and compare data transformations and normalization algorithms in multiplexed imaging data. Our methods adapt the ComBat and functional data registration methods to remove slide effects in this domain, and we present an evaluation framework to compare the proposed approaches. We present clear slide-to-slide variation in the raw, unadjusted data and show that many of the proposed normalization methods reduce this variation while preserving and improving the biological signal. Furthermore, we find that dividing multiplexed imaging data by its slide mean, and the functional data registration methods, perform the best under our proposed evaluation framework. In summary, this approach provides a foundation for better data quality and evaluation criteria in multiplexed imaging.

AVAILABILITY AND IMPLEMENTATION

Source code is provided at: https://github.com/statimagcoll/MultiplexedNormalization and an R package to implement these methods is available here: https://github.com/ColemanRHarris/mxnorm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Recent advances in spatially resolved transcriptomics: challenges and opportunities

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at singlemolecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capturebased method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization. [BMB Reports 2022; 55(3): 113-124].

CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data

Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more stable and higher accuracy with short execution time compared to other computational methods to predict the spatial location of excitatory neurons. CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.

A Primer for Single-Cell Sequencing in Non-Model Organisms

Single-cell sequencing technologies have led to a revolution in our knowledge of the diversity of cell types, connections between biological levels of organization, and relationships between genotype and phenotype. These advances have mainly come from using model organisms; however, using single-cell sequencing in non-model organisms could enable investigations of questions inaccessible with typical model organisms. This primer describes a general workflow for single-cell sequencing studies and considerations for using non-model organisms (limited to multicellular animals). Importantly, single-cell sequencing, when further applied in non-model organisms, will allow for a deeper understanding of the mechanisms between genotype and phenotype and the basis for biological variation.

T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10

Despite recent advances in cancer immunotherapy, certain tumor types, such as Glioblastomas, are highly resistant due to their tumor microenvironment disabling the anti-tumor immune response. Here we show, by applying an in-silico multidimensional model integrating spatially resolved and single-cell gene expression data of 45,615 immune cells from 12 tumor samples, that a subset of Interleukin-10-releasing HMOX1⁺ myeloid cells, spatially localizing to mesenchymal-like tumor regions, drive T-cell exhaustion and thus contribute to the immunosuppressive tumor microenvironment. These findings are validated using a human ex-vivo neocortical glioblastoma model inoculated with patient derived peripheral T-cells to simulate the immune compartment. This model recapitulates the dysfunctional transformation of tumor infiltrating T-cells. Inhibition of the JAK/STAT pathway rescues T-cell functionality both in our model and in-vivo, providing further evidence of IL-10 release being an important driving force of tumor immune escape. Our results thus show that integrative modelling of single cell and spatial transcriptomics data is a valuable tool to interrogate the tumor immune microenvironment and might contribute to the development of successful immunotherapies.

The tumour microenvironment counteracts immune therapy in Glioblastomas. Authors show here, using spatially resolved and single cell transcriptomics, that dysfunctional T cells are induced by a myeloid cell subset via Interleukin-10 signalling, and inhibition of the downstream JAK/STAT pathway might restore glioblastoma immune therapy responsiveness.

Spatial components of molecular tissue biology

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.

Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model

BACKGROUND

Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth.

METHODS

We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics.

RESULTS

An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition.

CONCLUSIONS

In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).

Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.

Cell-type modeling in spatial transcriptomics data elucidates spatially variable colocalization and communication between cell-types in mouse brain

Single-cell spatial transcriptomics (sc-ST) holds the promise to elucidate architectural aspects of complex tissues. Such analyses require modeling cell types in sc-ST datasets through their integration with single-cell RNA-seq datasets. However, this integration, is nontrivial since the two technologies differ widely in the number of profiled genes, and the datasets often do not share many marker genes for given cell types. We developed a neural network model, spatial transcriptomics cell-types assignment using neural networks (STANN), to overcome these challenges. Analysis of STANN's predicted cell types in mouse olfactory bulb (MOB) sc-ST data delineated MOB architecture beyond its morphological layer-based conventional description. We find that cell-type proportions remain consistent within individual morphological layers but vary significantly between layers. Notably, even within a layer, cellular colocalization patterns and intercellular communication mechanisms show high spatial variations. These observations imply a refinement of major cell types into subtypes characterized by spatially localized gene regulatory networks and receptor-ligand usage.

Cell2location maps fine-grained cell types in spatial transcriptomics

Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present сell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. We assessed cell2location in three different tissues and show improved mapping of fine-grained cell types. In the mouse brain, we discovered fine regional astrocyte subtypes across the thalamus and hypothalamus. In the human lymph node, we spatially mapped a rare pre-germinal center B cell population. In the human gut, we resolved fine immune cell populations in lymphoid follicles. Collectively, our results present сell2location as a versatile analysis tool for mapping tissue architectures in a comprehensive manner.

Deconvolution Tactics and Normalization in Renal Spatial Transcriptomics

The kidney is composed of heterogeneous groups of epithelial, endothelial, immune, and stromal cells, all in close anatomic proximity. Spatial transcriptomic technologies allow the interrogation of in situ expression signatures in health and disease, overlaid upon a histologic image. However, some spatial gene expression platforms have not yet reached single-cell resolution. As such, deconvolution of spatial transcriptomic spots is important to understand the proportion of cell signature arising from these varied cell types in each spot. This article reviews the various deconvolution strategies discussed in the 2021 Indiana O'Brien Center for Microscopy workshop. The unique features of Seurat transfer score methodology, SPOTlight, Robust Cell Type Decomposition, and BayesSpace are reviewed. The application of normalization and batch effect correction across spatial transcriptomic samples is also discussed.