Spatial transcriptomics iterative hierarchical clustering (stIHC): A novel method for identifying spatial gene co‐expression modules

Complex spatial and temporal patterns of gene expression underlie embryo differentiation, yet methods do not yet exist for the efficient genome-wide determination of spatial expression patterns during development. In situ imaging of transcripts and proteins is the gold-standard, but it is difficult and time consuming to apply to an entire genome, even when highly automated. Sequencing, in contrast, is fast and genome-wide, but is generally applied to homogenized tissues, thereby discarding spatial information. To take advantage of the efficiency and comprehensiveness of sequencing while retaining spatial information, we cryosectioned individual blastoderm stage Drosophila melanogaster embryos along the anterior-posterior axis and developed methods to reliably sequence the mRNA isolated from each 25 µm slice. The spatial patterns of gene expression we infer closely match patterns previously determined by in situ hybridization and microscopy. We applied this method to generate a genome-wide timecourse of spatial gene expression from shortly after fertilization through gastrulation. We identified numerous genes with spatial patterns that have not yet been described in the several ongoing systematic in situ based projects. This simple experiment demonstrates the potential for combining careful anatomical dissection with high-throughput sequencing to obtain spatially resolved gene expression on a genome-wide scale.

BackgroundDuring embryogenesis, signaling molecules produced by one cell population direct gene regulatory changes in neighboring cells and influence their developmental fates and spatial organization. One of the earliest events in the development of the vertebrate embryo is the establishment of three germ layers, consisting of the ectoderm, mesoderm and endoderm. Attempts to measure gene expression in vivo in different germ layers and cell types are typically complicated by the heterogeneity of cell types within biological samples (i.e., embryos), as the responses of individual cell types are intermingled into an aggregate observation of heterogeneous cell types. Here, we propose a novel method to elucidate gene regulatory circuits from these aggregate measurements in embryos of the frog Xenopus tropicalis using gene network inference algorithms and then test the ability of the inferred networks to predict spatial gene expression patterns.ResultsWe use two inference models with different underlying assumptions that incorporate existing network information, an ODE model for steady-state data and a Markov model for time series data, and contrast the performance of the two models. We apply our method to both control and knockdown embryos at multiple time points to reconstruct the core mesoderm and endoderm regulatory circuits. Those inferred networks are then used in combination with known dorsal-ventral spatial expression patterns of a subset of genes to predict spatial expression patterns for other genes. Both models are able to predict spatial expression patterns for some of the core mesoderm and endoderm genes, but interestingly of different gene subsets, suggesting that neither model is sufficient to recapitulate all of the spatial patterns, yet they are complementary for the patterns that they do capture.ConclusionThe presented methodology of gene network inference combined with spatial pattern prediction provides an additional layer of validation to elucidate the regulatory circuits controlling the spatial-temporal dynamics in embryonic development.

The isthmic organizer mediating differentiation of mid- and hindbrain during vertebrate development is characterized by a well-defined pattern of locally restricted gene expression domains around the mid-hindbrain boundary (MHB). This pattern is established and maintained by a regulatory network between several transcription and secreted factors that is not yet understood in full detail. In this contribution we show that a Boolean analysis of the characteristic spatial gene expression patterns at the murine MHB reveals key regulatory interactions in this network. Our analysis employs techniques from computational logic for the minimization of Boolean functions. This approach allows us to predict also the interplay of the various regulatory interactions. In particular, we predict a maintaining, rather than inducing, effect of Fgf8 on Wnt1 expression, an issue that remained unclear from published data. Using mouse anterior neural plate/tube explant cultures, we provide experimental evidence that Fgf8 in fact only maintains but does not induce ectopic Wnt1 expression in these explants. In combination with previously validated interactions, this finding allows for the construction of a regulatory network between key transcription and secreted factors at the MHB. Analyses of Boolean, differential equation and reaction-diffusion models of this network confirm that it is indeed able to explain the stable maintenance of the MHB as well as time-courses of expression patterns both under wild-type and various knock-out conditions. In conclusion, we demonstrate that similar to temporal also spatial expression patterns can be used to gain information about the structure of regulatory networks. We show, in particular, that the spatial gene expression patterns around the MHB help us to understand the maintenance of this boundary on a systems level.

Motivation:Drosophila melanogaster is a major model organism for investigating the function and interconnection of animal genes in the earliest stages of embryogenesis. Today, images capturing Drosophila gene expression patterns are being produced at a higher throughput than ever before. The analysis of spatial patterns of gene expression is most biologically meaningful when images from a similar time point during development are compared. Thus, the critical first step is to determine the developmental stage of an embryo. This information is also needed to observe and analyze expression changes over developmental time. Currently, developmental stages (time) of embryos in images capturing spatial expression pattern are annotated manually, which is time- and labor-intensive. Embryos are often designated into stage ranges, making the information on developmental time course. This makes downstream analyses inefficient and biological interpretations of similarities and differences in spatial expression patterns challenging, particularly when using automated tools for analyzing expression patterns of large number of images.Results: Here, we present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In an analysis of 3724 images, the new approach shows high accuracy in predicting the developmental stage correctly (79%). In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores for all images containing expression patterns of the same gene enable a direct way to view expression changes over developmental time for any gene. We show that the genomewide-expression-maps generated using images from embryos in refined stages illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.Availability and implementation: The software package is available for download at: http://www.public.asu.edu/∼jye02/Software/Fly-Project/.Contact:jieping.ye@asu.eduSupplementary information:Supplementary data are available at Bioinformatics online.

Background: Spatially resolved transcriptomics (ST) has enabled a variety of cancer research on the heterogeneity of tumor microenvironment. However, when identifying cancer boundaries based on ST, the limited resolution of ST such as Visium hinders the accurate identification of cancerous regions. To address these issues, we have developed a novel approach for accurately classifying regions of the tumor by using deep image prior (DIP) algorithm to convert Visium data into high-resolution images. Methods: We proposed SuperST, which utilizes deep image prior to transform low-resolution ST data into high-resolution images, providing more accurate spatial marker expression patterns. This method was used to define cancer regions by combining with a pre-trained deep learning algorithm that recognizes spatial patterns. ST data of hepatocellular carcinoma (HCC) patients were used to apply SuperST. Firstly, 400 spatially variable genes were identified. SuperST leverages the deep image prior (DIP) algorithm and a U-Net neural network to transform ST data into high-resolution ones without needing ground-truth high-resolution data. The final high-resolution images of gene expression data were used to extract 512-dimensional feature vectors for each gene via a pre-trained convolutional neural network model (VGG16). Subsequently, we apply K-means clustering to these features to identify transcriptionally distinct tissue regions considering spatial information and compared with the human-labeled cancer regions. Results: The cancer region detection using spatial gene expression data and our algorithm yielded results that substantially align with the human annotations on the H&E images. Furthermore, compared to the cancer definition obtained using the CopyKat and SPACET algorithms, our results were more spatially consistent for cancerous regions as well as providing high-resolution image-level definition of cancer region margin. This indicates that our algorithm better utilizes spatial information and more effectively overcomes the low resolution of ST data. Conclusion: This research demonstrates the effective application of our SuperST algorithm to the challenging problem of cancer region detection only using spatial gene expression data. It suggests that our algorithm could be utilized for a variety of problems in spatial biology in cancer research. Citation Format: Jeongbin Park, Seungho Cook, Dongjoo Lee, Jinyeong Choi, Seongjin Yoo, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2329.

Current standards for safe delivery of electrical stimulation to the central nervous system are based on foundational studies which examined post-mortem tissue for histological signs of damage. This set of observations and the subsequently proposed limits to safe stimulation, termed the “Shannon limits,” allow for a simple calculation (using charge per phase and charge density) to determine the intensity of electrical stimulation that can be delivered safely to brain tissue. In the three decades since the Shannon limits were reported, advances in molecular biology have allowed for more nuanced and detailed approaches to be used to expand current understanding of the physiological effects of stimulation. Here, we demonstrate the use of spatial transcriptomics (ST) in an exploratory investigation to assess the biological response to electrical stimulation in the brain. Electrical stimulation was delivered to the rat visual cortex with either acute or chronic electrode implantation procedures. To explore the influence of device type and stimulation parameters, we used carbon fiber ultramicroelectrode arrays (7 μm diameter) and microwire electrode arrays (50 μm diameter) delivering charge and charge density levels selected above and below reported tissue damage thresholds (range: 2–20 nC, 0.1–1 mC/cm2). Spatial transcriptomics was performed using Visium Spatial Gene Expression Slides (10x Genomics, Pleasanton, CA, United States), which enabled simultaneous immunohistochemistry and ST to directly compare traditional histological metrics to transcriptional profiles within each tissue sample. Our data give a first look at unique spatial patterns of gene expression that are related to cellular processes including inflammation, cell cycle progression, and neuronal plasticity. At the acute timepoint, an increase in inflammatory and plasticity related genes was observed surrounding a stimulating electrode compared to a craniotomy control. At the chronic timepoint, an increase in inflammatory and cell cycle progression related genes was observed both in the stimulating vs. non-stimulating microwire electrode comparison and in the stimulating microwire vs. carbon fiber comparison. Using the spatial aspect of this method as well as the within-sample link to traditional metrics of tissue damage, we demonstrate how these data may be analyzed and used to generate new hypotheses and inform safety standards for stimulation in cortex.

Recent advancements in high-throughput imaging have created new large datasets with tens of thousands of gene expression images. Methods for capturing these spatial and/or temporal expression patterns include in situ hybridization or fluorescent reporter constructs or tags, and results are still frequently assessed by subjective qualitative comparisons. In order to deal with available large datasets, fully automated analysis methods must be developed to properly normalize and model spatial expression patterns. We have developed image segmentation and registration methods to identify and extract spatial gene expression patterns from RNA in situ hybridization experiments of Drosophila embryos. These methods allow us to normalize and extract expression information for 78,621 images from 3724 genes across six time stages. The similarity between gene expression patterns is computed using four scoring metrics: mean squared error, Haar wavelet distance, mutual information and spatial mutual information (SMI). We additionally propose a strategy to calculate the significance of the similarity between two expression images, by generating surrogate datasets with similar spatial expression patterns using a Monte Carlo swap sampler. On data from an early development time stage, we show that SMI provides the most biologically relevant metric of comparison, and that our significance testing generalizes metrics to achieve similar performance. We exemplify the application of spatial metrics on the well-known Drosophila segmentation network. A Java webstart application to register and compare patterns, as well as all source code, are available from: http://tools.genome.duke.edu/generegulation/image_analysis/insitu uwe.ohler@duke.edu Supplementary data are available at Bioinformatics online.

Identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is an important first step toward characterizing the spatial transcriptomic landscape of complex tissues. Here we present a statistical method, SPARK, for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, providing effective control of type I errors and yielding high statistical power. With a computationally efficient algorithm, which is based on penalized quasi-likelihood, SPARK is also scalable to datasets with tens of thousands of genes measured on tens of thousands of samples. Analyzing four published spatially resolved transcriptomic datasets using SPARK, we show it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches.

Introduction Over the last decade, the technologies for analysing developmental mechanisms at the molecular and genetic level have improved dramatically for mammalian systems. The human genome is sequenced. Polymerase chain reaction techniques using very small amounts of tissue enable the determination of which genes are expressed in which tissue. In situ hybridization methods using cloned genes as probes display the spatial expression patterns of genes, and antibodies can be used to visualize the expression patterns of proteins. Transgenic approaches in the mouse are now highly sophisticated and microarray techniques will soon allow detailed comparison of expression patterns of genes in different cells and at different times in development, and proteomics permits comparisons of protein expression patterns between cells. So the questions are: do we really still need the more traditional model organisms, such as Drosophila and Caenorhabditis elegans, or should we only use vertebrate models such as zebraflsh and rodents? We will argue here that comparative approaches using invertebrate models are still extremely valuable, and will use as our example how the wealth of information on Drosophila development, particularly in oogenesis, could help us to unravel some of the key events in oogenesis in the mammal. We will also evaluate how good a model Drosophila is for mammalian oocyte development. The key advantages of Drosophila are its small size and easy maintenance in the laboratory, coupled with the wealth of genetic information already available for it. It is relatively easy to create and maintain mutants, even lethal ones. Traditional genetics allows genes to be arranged into developmental pathways and sophisticated techniques allow clones of cells to express mutant genes, so that the mutant changes they cause in specific groups of cells can be observed in the context of a whole organism. Arobust transformation system is available enabling analysis of promoters and enhancers, misexpression of genes and in vivo induced antisense to disrupt genes. Enhancer trapping techniques enable visualization of the spatial and temporal expression patterns of most genes. Our knowledge of the genetic control of development is thus very sophisticated. This, coupled with the fact that the genome is now sequenced and that annotation is well advanced, makes Drosophila an ideal organism in which to study development.

Next-generation sequencing enabled molecular landscaping and the discovery of novel tumor markers in ccRCC. However, the spatial relationships and histological features of the tumor microenvironment were lost during this process. Combining snRNA-seq, snATAC-seq, Spatial Transcriptomics (ST) technology, and immunofluorescence labeling, we discovered novel ccRCC progression tumor markers, and uncovered their spatial dependent expression pattern in both human ccRCC tumors and patient-derived xenograft (PDX). Using snRNA-seq and snATAC-seq, novel tumor progression markers such as NDRG1, MGST1, ABCC3 and PCSK6 were discovered, and their expressions were validated in ST. We found that one of the key novel tumor markers, CP, exhibited specific spatial expression patterns on tissue slides. The elevation of CP expression region is associated with a higher degree of hyalinization in ccRCC human tumor samples. Similarly, using immunofluorescence labeling, we validated the co-expression of CP and PCSK6 canonical ccRCC marker CA9. Overall, this study revealed novel ccRCC progression markers and linked their spatial expression pattern with histological features. Citation Format: Chia-Kuei Mo, Yige Wu, Terekhanova Nadezhda, Caravan Wagma, Preet Lal, Siqi Chen, Nataly Naser AL Deen, Ruiyang Liu, Yanyan Zhao, Kazuhito Sato, Lijun Yao, Mamatha Serasanambati, Andrew Shinkle, Reyka G. Jayasinghe, Li Ding, Feng Chen. Spatial transcriptomic profiling of progression markers in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2025.

Objective: Increasing evidence suggests that the tumor microenvironment (TME) of patients with triple negative breast cancer (TNBC) is hallmarked by extensive inter and intra-tumor heterogeneity. Deeper insights into the biological basis of this heterogeneity are needed to elucidate the aggressive nature of TNBC. Early attempts to stratify TNBC patients using bulk or single-cell transcriptomics have shown promise to understand differences in clinical outcomes and provide a personalized medicine approach. However, most examples limit themselves to a single 2D tissue slice per patient. Unfortunately, such an approach represents only a fraction of a 3D core needle biopsy, which is the only available tissue source to examine the treatment naïve TNBC microenvironment. Recently, spatial omics technologies demonstrated superior results for determining heterogeneity as these tools simultaneously profile the intact TME architecture and spatially resolved transcriptomes. Constructing 3D datasets by sectioning tissue and profiling each slice (n > 40) is feasible, but cost-prohibitive and impractical. An innovative digitization workflow that infers transcript localization across 3D datasets without probing each tissue slice would reduce costs and maximize tissue usage, whilst subsequently enhancing our understanding of tissue composition in the treatment naïve TNBC biopsy. Our goal is to facilitate building a comprehensive and large-scale map depicting TNBC heterogeneity from difficult-to-access tissue sources that could shed the much-needed light on TNBC. Method: We sectioned and imaged a treatment-naive core needle biopsy of TNBC into 48 serial slices, with 8 for spatial transcriptomics (Visium, 10X Genomics) equally spaced and 40 for H&E staining. Image coloration was normalized in a semi-automatic manner, and sections were registered on the associated H&E images using VALIS to create a 3D representation of the tissue. Transcripts from spatial transcriptomic sections were enhanced at super-resolution and mapped across serial sections using iSTAR. 3D spatial transcriptomic data analysis and quantitative comparisons with in-silico 2D slices was performed using the Giotto Suite ecosystem. Results: We generated a fully digital and super-enhanced (20 µm pixels) 3D spatial transcriptomics dataset from a single core needle TNBC biopsy by integrating image registration with spatial transcriptomics. Transcripts were projected across tissue sections using spatial interpolation and deep learning methods that learn the spatial and histology relationships for genes, thereby providing a comprehensive and super-resolved 3D representation. We observed that specific 3D structures, such as stromal plasma infiltrates and tertiary lymphoid structures, can be more accurately identified from the full 3D digital biopsy. We also illustrate how spatial gene expression patterns, key signaling pathways, and cellular architecture are organized in three dimensions, which cannot be easily inferred from 2D datasets alone. Importantly, systematic analysis comparing 3D versus 2D in-silico generated slices provides a quantitative framework that can be used to determine the limitations and power of spatial transcriptomic data analysis in 2D. Conclusion: This study demonstrates the feasibility of incorporating spatial transcriptomics and 3D registration methods into standard spatial analyses, enabling a detailed understanding of the spatial organization within a core needle TNBC biopsy and leading to informed insights on tissue architecture and function. Our approach facilitates a data-driven approach to better understand the strengths and limitations of profiling tumor heterogeneity in core needle biopsies of breast cancer. Finally, our approach enables researchers to make more informed research designs for large-scale projects, maximizing the usage of precious and often difficult-to-obtain patient samples. Citation Format: Jeffrey Sheridan, Jaiji George Chen, Junxiang Xu, Christina Ennis, Emma Kelley, Neal Kewalramani, Wonyl Choi, Jason Weiss, Gerald Denis, Naomi Ko, Ruben Dries. Accurate and cost-effective reconstruction of TNBC heterogeneity through 3D digitization of core needle biopsies [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P2-11-05.

Introduction. Tissue Microarrays (TMAs), widely utilized in the field of pathology, have now found a powerful ally in Image-based Spatial Transcriptomics (ST). By analyzing various gene expression data with high resolution, image-based ST data on TMA can provide the heterogeneous patterns of tumor microenvironment in multiple samples. However, an efficient method for processing multiple samples post-data acquisition is still under development. A streamlined process would expedite the discovery of spatial gene expression patterns, thereby enhancing our understanding of the tumor microenvironment and its implications for cancer diagnostics and treatment strategies. Methods. Our automated pipeline is initiated by automatic segmentation of every core in the whole TMA, derived from the processed output of the MERSCOPE or Xenium platform. Then, it subsequently removes QC failed cells unrelated to any core. Each core receives sequential naming and undergoes automated cell type mapping utilizing a reference single-cell RNAseq data. Additionally, the pipeline computes neighborhood enrichment between cell types, providing a nuanced comprehension of spatial relationships and interactions among diverse cell populations within the tissue microenvironment from multiple samples on the TMA. Results. The automated pipeline we've developed provides several key advantages. It enables the simultaneous analysis of multiple tissue cores, effectively minimizing batch effects and ensuring the reliability of results across diverse tissue samples. Furthermore, by automating core separation, labeling, and cell type mapping, our pipeline significantly streamlines the time and effort required for TMA data analysis. This cost-effective approach allows researchers to optimize resource allocation, making our automated pipeline a valuable tool in cancer research. It facilitates the exploration of gene expression patterns within specific tissue regions, ultimately contributing to the advancement of our understanding of cancer biology. Conclusion. By seamlessly integrating TMA data with image-based ST technologies such as Xenium and MERSCOPE, we can perform comprehensive spatial transcriptomics analyses and provide detailed statistical information on cell type proportions within the tumor microenvironment. This automated pipeline not only ensures robust and reliable results across diverse tissue samples but also optimizes resource allocation by minimizing batch effects and reducing analysis time. This cost-effective solution empowers researchers to delve deeper into spatial gene expression patterns, enhancing our comprehension of the tumor microenvironment. Citation Format: Dongjoo Lee, Seungho Cook, Yeonjae Jung, Myunghyun Lim, Jae Eun Lee, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4948.

Characterization and expression patterns of the MAPK family in zebrafish

Expression of a transgene containing 2.25 kb of the 5' flanking region of the rat Clara cell secretory protein gene and the human growth hormone gene was examined in developing mice. Despite an absolute preservation of tissue specificity based on RNA blot analysis, transgene-specific transcripts were detectable as early as 12.5 days of gestation, at least 4 days prior to endogenous Clara cell secretory protein gene expression. As differentiation proceeded, in situ hybridization revealed an increasingly restricted pattern of transgene expression in the developing pulmonary epithelium, such that by day 16.5 of gestation endogenous and transgene expression were confined to identical cells within the bronchiolar epithelium. The temporal discordance in transgene expression suggests the presence of unique cis-acting elements within the Clara cell secretory protein gene, not present in the transgene, which transduce developmental timing within pulmonary epithelium by actively repressing Clara cell secretory protein gene expression during early development. The unique expression of this transgene serves as a lineage marker in the respiratory epithelium and unmasks a temporal and spatial pattern of gene expression not observed in any pulmonary genes.

Computational methods have been proposed to leverage spatially resolved transcriptomic data, pinpointing genes with spatial expression patterns and delineating tissue domains. However, existing approaches fall short in uniformly quantifying spatially variable genes (SVGs). Moreover, from a methodological viewpoint, while SVGs are naturally associated with depicting spatial domains, they are technically dissociated in most methods. Here, we present a framework (PROST) for the quantitative recognition of spatial transcriptomic patterns, consisting of (i) quantitatively characterizing spatial variations in gene expression patterns through the PROST Index; and (ii) unsupervised clustering of spatial domains via a self-attention mechanism. We demonstrate that PROST performs superior SVG identification and domain segmentation with various spatial resolutions, from multicellular to cellular levels. Importantly, PROST Index can be applied to prioritize spatial expression variations, facilitating the exploration of biological insights. Together, our study provides a flexible and robust framework for analyzing diverse spatial transcriptomic data.