Spatial Transcriptomics Technologies Research Articles

Abstract C084: Oncogenic KRAS signaling drives the activation of tissue-resident fibroblasts and is required to maintain CAF heterogeneity in pancreatic cancer

Abstract The complexity of the tumor microenvironment (TME) is one of the distinguishing features of pancreatic ductal adenocarcinoma (PDA) and is responsible for patients’ poor response to therapies. The heterogeneity of cancer-associated fibroblasts (CAFs) has been correlated to the key features of the stroma that contribute to making PDA the third-leading cause of cancer-related deaths in the United States. The recent development of FDA-approved drugs against oncogenic KRAS opened a new therapeutic avenue for the treatment of tumors that have KRAS as their driver oncogene, such as PDA. However, the precise mechanisms driving the development of the TME and the contribution of KRAS to these processes have yet to be elucidated. This poses a challenge for the prediction of the effects of KRAS inhibition on established PDA tumors. By employing spatial transcriptomic technologies on various murine models recapitulating different stages of tumor initiation and progression, from acute and chronic inflammation to PanIN and overt PDA, we were able to observe distinctive changes in the activation status of pancreatic fibroblasts. These resident activated fibroblasts (RAFs) display expression of discrete markers brought upon by inflammation (inflammatory RAFs, iRAFs) or specific to oncogenic KRAS activation (myofibroblastic RAFs, myRAFs). Importantly, these RAF populations are maintained in established tumors and are identifiable in human PDA. To assess the consequences of the disruption of KRAS signaling on CAFs and RAFs, we employed a PDA mouse model that allows for the irreversible excision of KrasG12V, the FPC model (KrasFrt-LSL-G12V-Frt; p53LSL-R172H; PDX-CRE; Rosa26FlpOERT2). Through a combination of single-cell RNA sequencing (scRNA-seq), spatial transcriptomics and fluorescent in situ hybridization combined with immunofluorescence (immunoFISH) on FPC mice before and after KrasG12V deletion, we demonstrate the profound impact of Kras ablation on the TME composition. A deep remodeling of the stroma as well as significant changes in the proportion of CAF subtypes were evident. Interestingly, we observed a decrease in CAFs with a concomitant increase in RAFs. Our results suggest that the activation of oncogenic Kras in pre-neoplastic lesions drives a unique paracrine signaling that shapes the TME and is required to maintain the CAF population in PDA. Citation Format: Giuseppina Caligiuri, Jennifer S. Thalappillil, Mojdeh Shakiba, Sandeep Nadella, Juliene Hinds, Elise Courtois, William F. Flynn, Brinda Alagesan, Georgi N. Yordanov, Benjamin Kaminow, Paul Robson, Pedro A. Perez-Mancera, Jonathan Preall, Alexander Dobin, Youngku Park, David A. Tuveson. Oncogenic KRAS signaling drives the activation of tissue-resident fibroblasts and is required to maintain CAF heterogeneity in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C084.

TMIC-02. PRE-SURGICAL IMMUNE CHECKPOINT BLOCKADE RESHAPES THE IMMUNE LANDSCAPE AND SPATIAL ARCHITECTURE OF METASTATIC BRAIN TUMORS

Abstract Brain tumors reside in a modified microenvironment where inflammatory processes are heavily regulated. Nevertheless, our recent study showed a significant immune infiltration in recurrent glioblastoma (rGBM) treated with neoadjuvant PD-1 checkpoint blockade therapy. In comparison to GBM, the intracranial response of brain metastases (BrM) to immunotherapy has been less well studied. To test this question, we investigated the effect of pre-surgical immune checkpoint blockade (ICB) on the immune compartments of BrM, using multiplex immunofluorescence, single-cell RNA sequencing and spatial transcriptomics on a total of 17 patients. We found that ICB naïve BrM featured an immune exclusion phenotype with accumulation of immune cells in the fibrovascular stroma, necrotic and hemorrhagic peritumoral areas. Following ICB treatment, T cells and monocyte-derived macrophages were then observed within the tumor parenchyma. Computational receptor-ligand analysis suggested that the tumor-infiltrating T cells and macrophages were highly engaged by checkpoint receptor-ligand pairing, which may drive the T cells into an advanced exhausted state. To further explore the organization and interactions between the immune, stroma and tumor cells in BrM, we utilized the sequencing-based spatial transcriptomics technology. Our spatial transcriptomics data further highlighted the distinct spatial architectures between ICB naïve and –treated BrM tumors. It also pinpointed a unique macrophage subset that are preferentially co-localized with the brain vasculature cells and confined the immune infiltrates to the perivascular space. In summary, our study revealed ICB-induced marked changes in the immune composition and spatial organization in BrM and offers insights into new combination therapies that can help improve the clinical efficacy of immunotherapy for BrM patients.

Re-exploring tissue regeneration by novel spatial transcriptomics technologies.

Re-exploring tissue regeneration by novel spatial transcriptomics technologies.

51. Discerning cell types and states in spatial transcriptomics data using topic modelling

Glioblastoma multiforme (GBM) is the most common adult brain tumour. GBM cells include a diverse collection of normal and tumour cells with distinct molecular capabilities and with differential levels of sensitivity to treatment that led to treatment resistance. Spatial transcriptomics technologies (e.g. 10X Visium) have enabled analyzing tumour-normal cells interactions in a physical tissue context. Using Visium, a well-annotated cohort of adult GBM patient samples grown as patient-derived xenograft (PDX) models were profiled at different time points of the disease. However, the identification of cell types or states of Visium spots is a challenging problem as each spot contains a mixture of cells. Current tools for cell-type deconvolution are limited in discovering new novel cell types because they require a well-annotated reference dataset. Moreover, the PDX data has a mixture of human and mouse cell types at the same spot. In this work, we have applied topic modelling, a well-known probabilistic approach in natural language processing to discover the distribution of topics in language documents. Using the analogy between documents and spatial transcriptomic data, we were able to identify the main cell types in spatial PDX data without gene expression references dataset of tumour states, normal mouse, and immune cell types. Finally, we compared various probabilistic topic modelling algorithms to fit the PDX data, and to understand the cell-to-cell interactions between tumour and normal cells.

Spatial transcriptomics technology in cancer research.

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

Spatial transcriptomics tools allow for regional exploration of heterogeneous muscle pathology in the pre-clinical rabbit model of rotator cuff tear

BackgroundConditions affecting skeletal muscle, such as chronic rotator cuff tears, low back pain, dystrophies, and many others, often share changes in muscle phenotype: intramuscular adipose and fibrotic tissue increase while contractile tissue is lost. The underlying changes in cell populations and cell ratios observed with these phenotypic changes complicate the interpretation of tissue-level transcriptional data. Novel single-cell transcriptomics has limited capacity to address this problem because muscle fibers are too long to be engulfed in single-cell droplets and single nuclei transcriptomics are complicated by muscle fibers’ multinucleation. Therefore, the goal of this project was to evaluate the potential and challenges of a spatial transcriptomics technology to add dimensionality to transcriptional data in an attempt to better understand regional cellular activity in heterogeneous skeletal muscle tissue.MethodsThe 3′ Visium spatial transcriptomics technology was applied to muscle tissue of a rabbit model of rotator cuff tear. Healthy control and tissue collected at 2 and 16 weeks after tenotomy was utilized and freshly snap frozen tissue was compared with tissue stored for over 6 years to evaluate whether this technology is retrospectively useful in previously acquired tissues. Transcriptional information was overlayed with standard hematoxylin and eosin (H&E) stains of the exact same histological sections.ResultsSequencing saturation and number of genes detected was not affected by sample storage duration. Unbiased clustering matched the underlying tissue type-based on H&E assessment. Connective-tissue-rich areas presented with lower unique molecular identifier counts are compared with muscle fibers even though tissue permeabilization was standardized across the section. A qualitative analysis of resulting datasets revealed heterogeneous fiber degeneration–regeneration after tenotomy based on (neonatal) myosin heavy chain 8 detection and associated differentially expressed gene analysis.ConclusionsThis protocol can be used in skeletal muscle to explore spatial transcriptional patterns and confidently relate them to the underlying histology, even for tissues that have been stored for up to 6 years. Using this protocol, there is potential for novel transcriptional pathway discovery in longitudinal studies since the transcriptional information is unbiased by muscle composition and cell type changes.

The expanding vistas of spatial transcriptomics.

The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput. As such, they have enabled the determination of the cell-type architecture of tissues, the querying of cell-cell interactions and the monitoring of molecular interactions between tissue components. The rapidly evolving spatial genomics landscape will enable generalized high-throughput genomic measurements and perturbations to be performed in the context of tissues. These advances will empower hypothesis generation and biological discovery and bridge the worlds of tissue biology and genomics.

SpaceX: gene co-expression network estimation for spatial transcriptomics.

The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains. We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data. The SpaceX R-package is available at github.com/bayesrx/SpaceX. Supplementary data are available at Bioinformatics online.

Quantification of spatial pharmacogene expression heterogeneity in breast tumors.

Chemotherapeutic drug concentrations vary across different regions of tumors and this is thought to be involved in development of chemotherapy resistance. Insufficient drug delivery to some regions of the tumor may be due to spatial differences in expression of genes involved in the disposition, transport, and detoxification of drugs (pharmacogenes). Therefore, in this study, we analyzed the spatial expression of 286 pharmacogenes in six breast cancer tissues using the recently developed Visium spatial transcriptomics platform to (1) determine if these pharmacogenes are expressed heterogeneously across tumor tissue and (2) to determine which pharmacogenes have the most spatial expression heterogeneity. The spatial transcriptomics technology sequences the transcriptome of 55 um diameter barcoded sections (spots) across a tissue sample. We analyzed spatial gene expression profiles of four biobank-sourced breast tumor samples in addition to two breast tumor sample datasets from 10× Genomics. We define heterogeneity as the interquartile range of read counts. Collectively, we identified 8887 spots in tumor regions, 3814 in stroma, 44 in lymphocytes, and 116 in normal regions based on pathologist annotation of the tissues. We showed statistically significant differences in expression of pharmacogenes in tumor regions compared to surrounding non-tumor regions. We also observed that the most heterogeneously expressed genes within tumor regions were involved in reactive oxygen species (ROS)handling and detoxification mechanisms. GPX4, GSTP1, MGST3, SOD1, CYP4Z1, CYB5R3, GSTK1, and NAT1 showed the most heterogeneous expression within tumor regions. The heterogeneous expression of these pharmacogenes may have important implications for cancer therapy due to their ability to impact drug distribution and efficacy throughout the tumor. Our results suggest that chemoresistance caused by expression of GPX4, GSTP1, MGST3, and SOD1 may be intrinsic, not acquired, since the heterogeneity is not specific to chemotherapy-treated samples or cell type. Additionally, we identified candidate chemoresistance pharmacogenes that can be further tested through focused follow-up studies.

Single‐cell nucleic acid sequencing reinforces to reveal signal‐ dependent pathways contributing to cellular heterogeneity

Single‐cell nucleic acid sequencing reinforces to reveal signal‐ dependent pathways contributing to cellular heterogeneity

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.

Spatial Transcriptomics for Tumor Heterogeneity Analysis.

The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes. Although single-cell sequencing has become an important tool for investigating tumor heterogeneity recently, it lacks the spatial information of analyzed cells. In this regard, spatial transcriptomics holds great promise in deciphering the complex heterogeneity of cancer by providing localization-indexed gene expression information. This study reviews the applications of spatial transcriptomics in the study of tumor heterogeneity, discovery of novel spatial-dependent mechanisms, tumor immune microenvironment, and matrix microenvironment, as well as the pathological classification and prognosis of cancer. Finally, future challenges and opportunities for spatial transcriptomics technology’s applications in cancer are also discussed.

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability

RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.

Abstract 456: Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors

Abstract For over 100 years, the traditional tools of pathology, such as tissue-marking dyes (e.g. the H&E stain) have been used to study the disorganization and dysfunction of cells within tissues. This has represented a principal diagnostic and prognostic tool in cancer. However, in the last 5 years, new technologies have promised to revolutionize histopathology, with Spatial Transcriptomics technologies allowing us to measure gene expression directly in pathology-stained tissue sections. In parallel with these developments, Artificial Intelligence (AI) applied to histopathology tissue images now approaches pathologist level performance in cell type identification. However, these new technologies still have severe limitations, with Spatial Transcriptomics suffering difficulties distinguishing transcriptionally similar cell types, and AI-based pathology tools often performing poorly on real world out-of-batch test datasets. Thus, century-old techniques still represent standard-of-care in most areas of clinical cancer diagnostics and prognostics. Here, we present a new frontier in digital pathology: describing a conceptually novel computational methodology, based on Bayesian probabilistic modelling, that allows Spatial Transcriptomics data to be leveraged together with the output of deep learning-based AI used to computationally annotate H&E-stained sections of the same tumor. By leveraging cell-type annotations from multiple independent pathologists, we show that this integrated methodology achieves better performance than any given pathologist’s manual tissue annotation in the task of identifying regions of immune cell infiltration in breast cancer, and easily outperforms either technology alone. We also show that on a subset of histopathology slides examined, the methodology can identify regions of clinically relevant immune cell infiltration that were missed entirely by an initial pathologist’s manual annotation. While this use case has clear diagnostic and prognostic value in cancer (e.g. predicting response to immunotherapy), our methodology is generalizable to any type of pathology images and also has broad applications in spatial transcriptomics data analytics, where most applications (such as identifying cell-cell interactions) rely on correct cell type annotations having been established a priori. We anticipate that this work will spur many follow-up studies, including new computational innovations building on the approach. The work sets the stage for better-than-pathologist performance in other cell-type annotation tasks, with relevant applications in diagnostics and prognostics across almost all cancers. Citation Format: Asif Zubair, Rich Chapple, Sivaraman Natarajan, William C. Wright, Min Pan, Hyeong-Min Lee, Heather Tillman, John Easton, Paul Geeleher. Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors [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 456.

Cell type matching in single-cell RNA-sequencing data using FR-Match

Reference cell atlases powered by single cell and spatial transcriptomics technologies are becoming available to study healthy and diseased tissue at single cell resolution. One important use of these data resources is to compare cell types from new dataset with cell types in the reference atlases to evaluate their phenotypic similarities and differences, for example, for identifying novel cell types under disease conditions. For this purpose, rigorously-validated computational algorithms are needed to perform these cell type matching tasks that can compare datasets from different experiment platforms and sample types. Here, we present significant enhancements to FR-Match (v2.0)—a multivariate nonparametric statistical testing approach for matching cell types in query datasets to reference atlases. FR-Match v2.0 includes a normalization procedure to facilitate cross-platform cluster-level comparisons (e.g., plate-based SMART-seq and droplet-based 10X Chromium single cell and single nucleus RNA-seq and spatial transcriptomics) and extends the pipeline to also allow cell-level matching. In the use cases evaluated, FR-Match showed robust and accurate performance for identifying common and novel cell types across tissue regions, for discovering sub-optimally clustered cell types, and for cross-platform and cross-sample cell type matching.

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.

Modeling zero inflation is not necessary for spatial transcriptomics

BackgroundSpatial transcriptomics are a set of new technologies that profile gene expression on tissues with spatial localization information. With technological advances, recent spatial transcriptomics data are often in the form of sparse counts with an excessive amount of zero values.ResultsWe perform a comprehensive analysis on 20 spatial transcriptomics datasets collected from 11 distinct technologies to characterize the distributional properties of the expression count data and understand the statistical nature of the zero values. Across datasets, we show that a substantial fraction of genes displays overdispersion and/or zero inflation that cannot be accounted for by a Poisson model, with genes displaying overdispersion substantially overlapped with genes displaying zero inflation. In addition, we find that either the Poisson or the negative binomial model is sufficient for modeling the majority of genes across most spatial transcriptomics technologies. We further show major sources of overdispersion and zero inflation in spatial transcriptomics including gene expression heterogeneity across tissue locations and spatial distribution of cell types. In particular, when we focus on a relatively homogeneous set of tissue locations or control for cell type compositions, the number of detected overdispersed and/or zero-inflated genes is substantially reduced, and a simple Poisson model is often sufficient to fit the gene expression data there.ConclusionsOur study provides the first comprehensive evidence that excessive zeros in spatial transcriptomics are not due to zero inflation, supporting the use of count models without a zero inflation component for modeling spatial transcriptomics.

Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model.

Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.

Reference-free cell\xa0type 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.

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 ).