Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization

SUMMARY

Spatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation.

AVAILABILITY AND IMPLEMENTATION

The R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.

Solid-phase capture and profiling of open chromatin by spatial ATAC

Current methods for epigenomic profiling are limited in their ability to obtain genome-wide information with spatial resolution. We introduce spatial ATAC, a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery of the regulatory programs underlying spatial gene expression during mouse organogenesis, lineage differentiation and in human pathology.

Spatial transcriptomics of human placentas reveal distinct RNA patterns associated with morphology and preeclampsia

Spatial transcriptomics (ST) maps RNA level patterns within a tissue. This technology has not been previously applied to human placental tissue. We demonstrate analysis of human placental samples with ST. Unsupervised clustering revealed that distinct RNA patterns were found corresponding to different morphological structures. Additionally, when focusing upon terminal villi and hemoglobin associated structures, RNA levels differed between placentas from full term healthy pregnancies and those complicated by preeclampsia. The results from this study can provide a benchmark for future ST studies in placenta.

Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium

The inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics, where tissue-resident RNA is spatially labeled in situ with barcodes in a transcriptome-wide fashion, to study local tissue interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-Seq data coupled to cell type-specific localization patterns at and around organized structures of infiltrating leukocyte cells in the synovium. Combining morphological features and high-throughput spatially resolved transcriptomics may be able to provide higher statistical power and more insights into monitoring disease severity and treatment-specific responses in seropositive and seronegative rheumatoid arthritis.

Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin

The contribution of cellular heterogeneity and architecture to white adipose tissue (WAT) function is poorly understood. Herein, we combined spatially resolved transcriptional profiling with single-cell RNA sequencing and image analyses to map human WAT composition and structure. This identified 18 cell classes with unique propensities to form spatially organized homo- and heterotypic clusters. Of these, three constituted mature adipocytes that were similar in size, but distinct in their spatial arrangements and transcriptional profiles. Based on marker genes, we termed these Adipo^LEP, Adipo^PLIN, and Adipo^SAA. We confirmed, in independent datasets, that their respective gene profiles associated differently with both adipocyte and whole-body insulin sensitivity. Corroborating our observations, insulin stimulation in vivo by hyperinsulinemic-euglycemic clamp showed that only Adipo^PLIN displayed a transcriptional response to insulin. Altogether, by mining this multimodal resource we identify that human WAT is composed of three classes of mature adipocytes, only one of which is insulin responsive.

Divergent clonal differentiation trajectories establish CD8+ memory T cell heterogeneity during acute viral infections in humans

The CD8⁺ T cell response to an antigen is composed of many T cell clones with unique T cell receptors, together forming a heterogeneous repertoire of effector and memory cells. How individual T cell clones contribute to this heterogeneity throughout immune responses remains largely unknown. In this study, we longitudinally track human CD8⁺ T cell clones expanding in response to yellow fever virus (YFV) vaccination at the single-cell level. We observed a drop in clonal diversity in blood from the acute to memory phase, suggesting that clonal selection shapes the circulating memory repertoire. Clones in the memory phase display biased differentiation trajectories along a gradient from stem cell to terminally differentiated effector memory fates. In secondary responses, YFV- and influenza-specific CD8⁺ T cell clones are poised to recapitulate skewed differentiation trajectories. Collectively, we show that the sum of distinct clonal phenotypes results in the multifaceted human T cell response to acute viral infections.

A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart

The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.

Identification and transfer of spatial transcriptomics signatures for cancer diagnosis

BACKGROUND

Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types.

METHODS

We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles.

RESULTS

We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC.

CONCLUSIONS

This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future.

Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics

Lately it has become possible to analyze transcriptomic profiles in tissue sections with retained cellular context. We aimed to explore synovial biopsies from rheumatoid arthritis (RA) and spondyloarthritis (SpA) patients, using Spatial Transcriptomics (ST) as a proof of principle approach for unbiased mRNA studies at the site of inflammation in these chronic inflammatory diseases. Synovial tissue biopsies from affected joints were studied with ST. The transcriptome data was subjected to differential gene expression analysis (DEA), pathway analysis, immune cell type identification using Xcell analysis and validation with immunohistochemistry (IHC). The ST technology allows selective analyses on areas of interest, thus we analyzed morphologically distinct areas of mononuclear cell infiltrates. The top differentially expressed genes revealed an adaptive immune response profile and T-B cell interactions in RA, while in SpA, the profiles implicate functions associated with tissue repair. With spatially resolved gene expression data, overlaid on high-resolution histological images, we digitally portrayed pre-selected cell types in silico. The RA displayed an overrepresentation of central memory T cells, while in SpA effector memory T cells were most prominent. Consequently, ST allows for deeper understanding of cellular mechanisms and diversity in tissues from chronic inflammatory diseases.

High-definition spatial transcriptomics for in situ tissue profiling

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-μm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections

Spatial resolution of gene expression enables gene expression events to be pinpointed to a specific location in biological tissue. Spatially resolved gene expression in tissue sections is traditionally analyzed using immunohistochemistry (IHC) or in situ hybridization (ISH). These technologies are invaluable tools for pathologists and molecular biologists; however, their throughput is limited to the analysis of only a few genes at a time. Recent advances in RNA sequencing (RNA-seq) have made it possible to obtain unbiased high-throughput gene expression data in bulk. Spatial Transcriptomics combines the benefits of traditional spatially resolved technologies with the massive throughput of RNA-seq. Here, we present a protocol describing how to apply the Spatial Transcriptomics technology to mammalian tissue. This protocol combines histological staining and spatially resolved RNA-seq data from intact tissue sections. Once suitable tissue-specific conditions have been established, library construction and sequencing can be completed in ~5-6 d. Data processing takes a few hours, with the exact timing dependent on the sequencing depth. Our method requires no special instruments and can be performed in any laboratory with access to a cryostat, microscope and next-generation sequencing.

Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing

Accurate variant calling and genotyping represent major limiting factors for downstream applications of single-cell genomics. Here, we report Conbase for the identification of somatic mutations in single-cell DNA sequencing data. Conbase leverages phased read data from multiple samples in a dataset to achieve increased confidence in somatic variant calls and genotype predictions. Comparing the performance of Conbase to three other methods, we find that Conbase performs best in terms of false discovery rate and specificity and provides superior robustness on simulated data, in vitro expanded fibroblasts and clonal lymphocyte populations isolated directly from a healthy human donor.

ST Pipeline: an automated pipeline for spatial mapping of unique transcripts

Motivation

In recent years we have witnessed an increase in novel RNA-seq based techniques for transcriptomics analysis. Spatial transcriptomics is a novel RNA-seq based technique that allows spatial mapping of transcripts in tissue sections. The spatial resolution adds an extra level of complexity, which requires the development of new tools and algorithms for efficient and accurate data processing.

Results

Here we present a pipeline to automatically and efficiently process RNA-seq data obtained from spatial transcriptomics experiments to generate datasets for downstream analysis.

Availability and implementation

The ST Pipeline is open source under a MIT license and it is available at https://github.com/SpatialTranscriptomicsResearch/st_pipeline.

Contact

jose.fernandez.navarro@scilifelab.se.

Supplementary information

Supplementary data are available at Bioinformatics online.

Spatial detection of fetal marker genes expressed at low level in adult human heart tissue

Heart failure is a major health problem linked to poor quality of life and high mortality rates. Hence, novel biomarkers, such as fetal marker genes with low expression levels, could potentially differentiate disease states in order to improve therapy. In many studies on heart failure, cardiac biopsies have been analyzed as uniform pieces of tissue with bulk techniques, but this homogenization approach can mask medically relevant phenotypes occurring only in isolated parts of the tissue. This study examines such spatial variations within and between regions of cardiac biopsies. In contrast to standard RNA sequencing, this approach provides a spatially resolved transcriptome- and tissue-wide perspective of the adult human heart, and enables detection of fetal marker genes expressed by minor subpopulations of cells within the tissue. Analysis of patients with heart failure, with preserved ejection fraction, demonstrated spatially divergent expression of fetal genes in cardiac biopsies.

Spatially resolved transcriptome profiling in model plant species

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study high-resolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes high-throughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call “spatial transcriptomics,” that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

A novel method for rapid hybridization of DNA to a solid support

Here we present a novel approach entitled Magnetic Forced Hybridization (MFH) that provides the means for efficient and direct hybridization of target nucleic acids to complementary probes immobilized on a glass surface in less than 15 seconds at ambient temperature. In addition, detection is carried out instantly since the beads become visible on the surface. The concept of MFH was tested for quality control of array manufacturing, and was combined with a multiplex competitive hybridization (MUCH) approach for typing of Human Papilloma Virus (HPV). Magnetic Forced Hybridization of bead-DNA constructs to a surface achieves a significant reduction in diagnostic testing time. In addition, readout of results by visual inspection of the unassisted eye eliminates the need for additional expensive instrumentation. The method uses the same set of beads throughout the whole process of manipulating and washing DNA constructs prior to detection, as in the actual detection step itself.

Toward the single-hour high-quality genome

Today, resequencing of a human genome can be performed in approximately a week using a single instrument. Thanks to a steady logarithmic rate of increase in performance for DNA sequencing platforms over the past seven years, DNA sequencing is one of the fastest developing technology fields. As the process becomes faster, it opens up possibilities within health care, diagnostics, and entirely new fields of research. Immediate genetic characterization of contagious outbreaks has been exemplified, and with such applications for the direct benefit of human health, expectations of future sensitive, rapid, high-throughput, and cost-effective technologies are steadily growing. Simultaneously, some of the limitations of a rapidly growing field have become apparent, and questions regarding the quality of some of the data deposited into databases have been raised. A human genome sequenced in only an hour is likely to become a reality in the future, but its definition may not be as certain.

Visual DNA as a diagnostic tool

We report on the incorporation of the Visual DNA concept in a genotyping assay as a simple and straightforward detection tool. The principle of trapping streptavidin-coated superparamagnetic beads of micrometer size for visualization of genetic variances is used for PrASE-based detection of a panel of mutations in the severe and common genetic disorder of cystic fibrosis. The method allows a final investigation of genotypes by the naked eye and the output is easily documented using a regular hand-held device with an integrated digital camera. A number of samples were run through the assay, showing rapid and accurate detection using superparamagnetic beads and an off-the-shelf neodymium magnet. The assay emphasizes the power of Visual DNA and demonstrates the potential value of the method in future point-of-care tests.

Flow cytometry for enrichment and titration in massively parallel DNA sequencing

Massively parallel DNA sequencing is revolutionizing genomics research throughout the life sciences. However, the reagent costs and labor requirements in current sequencing protocols are still substantial, although improvements are continuously being made. Here, we demonstrate an effective alternative to existing sample titration protocols for the Roche/454 system using Fluorescence Activated Cell Sorting (FACS) technology to determine the optimal DNA-to-bead ratio prior to large-scale sequencing. Our method, which eliminates the need for the costly pilot sequencing of samples during titration is capable of rapidly providing accurate DNA-to-bead ratios that are not biased by the quantification and sedimentation steps included in current protocols. Moreover, we demonstrate that FACS sorting can be readily used to highly enrich fractions of beads carrying template DNA, with near total elimination of empty beads and no downstream sacrifice of DNA sequencing quality. Automated enrichment by FACS is a simple approach to obtain pure samples for bead-based sequencing systems, and offers an efficient, low-cost alternative to current enrichment protocols.

Allelotyping by massively parallel pyrosequencing of SNP-carrying trinucleotide threads

Here we present an approach for allelotyping combining the multiplexing features of the trinucleotide threading (TnT) method with pooling of genomic DNA and massively parallel pyrosequencing, enabling reliable allele frequency estimation in large cohorts. The approach offers several benefits as compared to array-based methods and allows undertaking highly complex studies without compromising accuracy, while keeping the workload to a minimum. This proof-of-concept study involves formation of trinucleotide threads, targeting a total of 147 single-nucleotide polymorphisms (SNPs) related to obesity and cancer, for multiplex amplification and allele extraction from a pool of 462 genomes, followed by massively parallel pyrosequencing. Approximately 177k reads were approved, identified, and assigned to SNP-carrying threads rendering representative allele frequencies in the cohort.