Sountoulidis et al. provide a spatial gene expression atlas of human embryonic lung during the first trimester of gestation and identify 83 cell identities corresponding to stable cell types or transitional states. The lung contains numerous specialized cell types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a comprehensive topographic atlas of early human lung development. Here we report 83 cell states and several spatially resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated single-cell RNA sequencing and spatially resolved transcriptomics into a web-based, open platform for interactive exploration. We show distinct gene expression programmes, accompanying sequential events of cell differentiation and maturation of the secretory and neuroendocrine cell types in proximal epithelium. We define the origin of airway fibroblasts associated with airway smooth muscle in bronchovascular bundles and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

Previous large-scale studies have uncovered many features that determine the processing of microRNA (miRNA) precursors; however, they have been conducted in vitro. Here, we introduce MapToCleave, a method to simultaneously profile processing of thousands of distinct RNA structures in living cells. We find that miRNA precursors with a stable lower basal stem are more efficiently processed and also have higher expression in vivo in tissues from 20 animal species. We systematically compare the importance of known and novel sequence and structural features and test biogenesis of miRNA precursors from 10 animal and plant species in human cells. Lastly, we provide evidence that the GHG motif better predicts processing when defined as a structure rather than sequence motif, consistent with recent cryogenic electron microscopy (cryo-EM) studies. In summary, we apply a screening assay in living cells to reveal the importance of lower basal stem stability for miRNA processing and in vivo expression.

The field of spatial transcriptomics is rapidly expanding, and with it the repertoire of available technologies. However, several of the transcriptome-wide spatial assays do not operate on a single cell level, but rather produce data comprised of contributions from a – potentially heterogeneous – mixture of cells. Still, these techniques are attractive to use when examining complex tissue specimens with diverse cell populations, where complete expression profiles are required to properly capture their richness. Motivated by an interest to put gene expression into context and delineate the spatial arrangement of cell types within a tissue, we here present a model-based probabilistic method that uses single cell data to deconvolve the cell mixtures in spatial data. To illustrate the capacity of our method, we use data from different experimental platforms and spatially map cell types from the mouse brain and developmental heart, which arrange as expected.

Recent advances in spatially resolved transcriptomics have greatly expandedthe knowledge of complex multicellular biological systems. The field hasquickly expanded in recent years, and several new technologies have beendeveloped that all aim to combine gene expression data with spatialinformation. The vast array of methodologies displays fundamentaldierences in their approach to obtain this information, and thus,demonstrate method-specific advantages and shortcomings. While the field ismoving forward at a rapid pace, there are still multiple challenges presentedto be addressed, including sensitivity, labor extensiveness, tissue-typedependence, and limited capacity to obtain detailed single-cell information.No single method can currently address all these key parameters. In thisreview, available spatial transcriptomics methods are described and theirapplications as well as their strengths and weaknesses are discussed. Futuredevelopments are explored and where the field is heading to is deliberatedupon.

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.

Heart failure affects 2-3% of adult Western population. Prevalence of heart failure with preserved left ventricular (LV) ejection fraction (HFpEF) increases. Studies suggest HFpEF patients to have altered myocardial structure and functional changes such as incomplete relaxation and increased cardiac stiffness. We hypothesised that patients undergoing elective coronary bypass surgery (CABG) with HFpEF characteristics would show distinctive gene expression compared to patients with normal LV physiology. Myocardial biopsies for mRNA expression analysis were obtained from sixteen patients with LV ejection fraction >= 45%. Five out of 16 patients (31%) had echocardiographic characteristics and increased NTproBNP levels indicative of HFpEF and this group was used as HFpEF proxy, while 11 patients had Normal LV physiology. Utilising principal component analysis, the gene expression data clustered into two groups, corresponding to HFpEF proxy and Normal physiology, and 743 differentially expressed genes were identified. The associated top biological functions were cardiac muscle contraction, oxidative phosphorylation, cellular remodelling and matrix organisation. Our results also indicate that upstream regulatory events, including inhibition of transcription factors STAT4, SRF and TP53, and activation of transcription repressors HEY2 and KDM5A, could provide explanatory mechanisms to observed gene expression differences and ultimately cardiac dysfunction in the HFpEF proxy group.

Spatially resolved transcriptomics has greatly expanded our knowledge of complex multicellular biological systems. To date, several technologies have been developed that combine gene expression data with information about its spatial tissue context. There is as yet no single spatial method superior to all others, and the existing methods have jointly contributed to progress in this field of technology. Some challenges presented by existing protocols include having a limited number of targets, being labor extensive, being tissue-type dependent and having low throughput or limited resolution. Within the scope of this thesis, many aspects of these challenges have been taken into consideration, resulting in a detailed evaluation of a recently developed spatial transcriptome-wide method. This method, termed Spatial Transcriptomics (ST), enables the spatial location of gene activity to be preserved and visually links it to its histological position and anatomical context. Paper I describes all the details of the experimental protocol, which starts when intact tissue sections are placed on barcoded microarrays and finishes with high throughput sequencing. Here, spatially resolved transcriptome-wide data are obtained from both mouse olfactory bulb and breast cancer samples, demonstrating the broad tissue applicability and robustness of the approach. In Paper II, the ST technology is applied to samples of human adult heart, a tissue type that contains large proportions of fibrous tissue and thus makes RNA extraction substantially more challenging. New protocol strategies are optimized in order to generate spatially resolved transcriptome data from heart failure patients. This demonstrates the advantage of using the technology for the identification of lowly expressed biomarkers that have previously been seen to correlate with disease progression in patients suffering heart failure. Paper III shows that, although the ST technology has limited resolution compared to other techniques, it can be combined with single-cell RNA-sequencing and hence allow the spatial positions of individual cells to be recovered. The combined approach is applied to developing human heart tissue and reveals cellular heterogeneity of distinct compartments within the complete organ. Since the ST technology is based on the sequencing of mRNA tags, Paper IV describes a new version of the method, in which spatially resolved analysis of full-length transcripts is being developed. Exploring the spatial distribution of full-length transcripts in tissues enables further insights into alternative splicing and fusion transcripts and possible discoveries of new genes.  

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.

Analysis of the pattern of proteins or messenger RNAs (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.