Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.

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.

In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource, where isoform expression and RNA editing can be visualized in a spatial context.

Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer(1). Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics(2) to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

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

Transcriptomics promises biological insight into gene regulation, cell diversity, and mechanistic understanding of dysfunction. Driven by technological advancements in sequencing technologies, the field has witnessed an exponential growth in data output. Not only has the amount of raw data increased tremendously but it’s granularity as well. From only being able to obtain aggregated transcript information from large tissue samples, we can now pinpoint the precise origin of transcripts within the tissue, sometimes even within the confines of individual cells. This thesis focuses on the different aspects of how to use these emergent technologies to obtain a greater understanding of biological mechanisms. The work conducted here spans only a few years of the much longer history of spatially resolved transcriptomics, which started with the early in situ hybridization techniques and will continue to a potential future with complete molecular profiling ofevery cell in their natural, active state. Thus, at the same time the workpresented here introduces and demonstrates the use of the latest techniques within spatial transcriptomics, it also deals with the shortcomings of the current state of the field, which undoubtedly will see extensive improvements in the not too distant future. Article I is part of a series of articles where we mechanistically examine the biological underpinnings of a serendipitous finding that single-stranded nucleic acids have immunomodulatory effects. In particular, we look at influenza-infected innate immune cells and the ability of the oligonucleotide to inhibit viral entry. The oligonucleotides prevent the cells from responding to certain types of pattern recognitionand cause a decrease in viral load. Our hypothesis is that the administration of oligonucleotides blocks certain endocytic routes. While the invivo experiments suggest that the influenza virus is still able to infect and promote disease in the host, changes in signaling response due to the inhibition of the endocytotic routes could represent an avenue for future therapeutics. The conclusions were drawn by combining protein labeling and conventional methods for RNA profiling in the form of quantitative realtime PCR and bulk RNA sequencing. As a transition into the concept of spatial RNA profiling, the thesis includes an Additional material review article on spatial transcriptomics, where we give an overview of the current state of the field, as it looked like in the beginning of 2020. In Article II, we report on the development of an R package for analyzing spatial transcriptomics datasets. The package offers visualization features and an automated pipeline for masking tissue images and aligning serially sectioned experiments. The tool is extensively used throughout the rest of the articles where spatial transcript information is analyzed and is available for all scientists that use the supported spatial transcriptomics platforms in their research. In Article III, we propose a method to spatially map long-read sequencing data. While previously described methods for high-throughput spatial transcriptomics produce short-read data, full-length transcript information allows us to spatially profile alternatively spliced transcripts. Using the proposed method, we find alternatively spliced transcripts and find isoforms of the same gene to be differentially expressed in different regions of the mouse brain. Furthermore, we profile RNA editing across the full-length transcripts and find certain parts of the mouse left hemisphere to display a substantially higher degree of editing events compared to the rest of the brain. The proposed method is based on readily available reagents and does not require advanced instrumentation. We believe full-length transcript information obtained in this manner could help scientists obtain a deeper understanding from transcriptome data. Finally, in Article IV, we explore how the latest technologies for spatial transcriptomics can be used to characterize the expression landscape of respiratory syncytial virus infections by comparing infected and non-infected mouse lungs. By integration of annotated single-cell data and spatially resolved transcriptomics, we map the location of the single cells onto the spatial grid to localize immune cell populations across the tissue sections. By correlating the locations to gene expression, we profile locally confined cellular processes and immune responses. We believe that high-throughput spatial information obtained without predefined targets will become an important tool for exploratory analysis and hypothesis generation, which in turn could unlock mechanistic knowledge of the differences between experimental models that are important for translational research.

Läran om genuttryck tros kunna ge kunskap kring celldiversitet och en ökad mekanistisk förståelse för dysregulation. Detta fält, benämnt transkriptomik, har sett exponentiell tillväxt i mån av genererad data på senare år, till stor del drivet av teknologiska framsteg. Inte bara den råa mängden data har ökat, utan även förmågan att särskilja vilka celler som informationen om generna kommer ifrån. Historiskt har sådan information endast observerats utifrån större vävnadsbitar, och således har ett medelvärde över flertalet celler observerats, utan att veta från vilka celler de individuella observationerna härstammar eller cellernas inbördes lokalisation. Denna avhandling kretsar kring de nya metoderna för spatiell analys av transkriptomet, vilka möjliggör positionering av vart någonstans i vävnaden genuttrycket sker och på så vis ger den granularitet som verklig mekanistisk förståelse ofta kräver. Det arbete som presenteras här spänner endast över några år av den längre bana som utvecklingen av spatiell transkriptomik befinner sig på, från de tidiga experimenten av in situ hybridisering till en potentiell framtid med komplett molekylär profilering av varje cell i deras naturliga miljö. Då det senare än ej är realiserat idag, behandlar avhandlingen och de inkluderade arbeten även tillkortakommanden i dagens teknik. Detta fält är under mycket snabb utveckling, och flera av de svagheter som finns idag tros vara kraftigt förminskade inom en relativt snart framtid. Artikel I är en del av en serie av artiklar där vi mekanistiskt undersöker ett fenomen där enkelsträngade nukleinsyror medför immunomodulativa effekter. Mer specifikt undersöker vi i den aktuella artikeln hur oligonukleotider av särskild längd påverkar influenzainfekterade dendritiska celler och viruspartiklarnas möjlighet att ta sig in i dessa celler. Vi finner inhibering av cellernas förmåga att respondera till särskild mönsterigenkänning samt minskade virusmängder direkt efter administration av oligonukleotider. Vår hypotes är att detta är en effekt av blockering av särskilda endocytotiska vägar. Experiment i möss tyder på att influensaviruset fortfarande är kapabelt att infektera och medföra sjukdom hos djuren, men resultatet av att blockera de endocytotiska upptagsvägarna för viruset medför förändrad signalering, vilket kan utgöra en intressant möjlighet för terapeutiska interventioner. Slutsatserna dras genom att kombinera protein-infärgning och konventionella metoder för analys av transkriptomet, i form av kvantitativ realtids-PCR och bulk-RNA-sekvensering. En övergång till spatiell analys görs sedan, där en review på ämnet är inkluderad i avhandlingen som en bilaga, och fungerar som översikt över alla de metoder som tagits fram för att möjliggöra denna typ av analys, så som det såg ut i början av 2020. I Artikel II visar vi utvecklingen av en mjukvara skriven i R för sekvensbaserad spatiell transkriptomik. Mer specifikt adderar vi visualiseringsmöjligheter och en automatiserad pipeline för bildhantering. Verktyget är öppet tillgängligt för alla som använder de spatiella transkriptomik-plattformarna som stöds. I Artikel III vidareutvecklar vi protokollet för spatiell transkriptomik för att kunna utnyttja de teknologiska framstegen som skett inom sekvensering av fullängds-transkriptomik. Genom att läsa av hela transkript istället för endast kortare bitar, som är standard idag, kan transkriptomets fulla komplexitet analyseras. Exempelvis visar vi hur kvantiteter av olika isoformer av en och samma gen skiljer sig markant mellan olika regioner i mushjärnan samt hur vissa typer av RNA-förändringar är vanligare i olika regioner. Det föreslagna protokollet använder enkelt tillgängliga reagenser och kräver ingen avancerad mätutrustning. Vi tror att fullängds-information kommer att vara avgörande för att uppnå komplett biologisk förståelse utifrån transkriptomdata. Slutligen, i Artikel IV, använder vi de senaste metoderna för spatiell transkriptomik för att undersöka hur den lokala miljön i lungan påverkas av en viral infektion genom att jämföra genuttrycket mellan infekterade och icke-infekterade möss. Genom att integrera publikt tillgänglig annoterad data från enskilda celler me spatiell transkriptomdata, kartlägger vi hur olika typer av immunceller lokaliserar sig över vävnadssnitten. Genom att korrelera genuttryck och celltypernas position, skapar vi en uttömmande bild över hur olika cellulära processer och immunresponser uppvisar lokala anpassningar. Vi tror att storskalig spatial information utan fördefinierade val kring vilka gener som undersöks kommer att utgöra ett viktigt verktyg för explorativ analys och hypotesgenerering.  

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2(+)TMPRSS2(+) cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

An integrated analysis of over 100 single-cell and single-nucleus transcriptomics studies illustrates severe acute respiratory syndrome coronavirus 2 viral entry gene coexpression patterns across different human tissues, and shows association of age, smoking status and sex with viral entry gene expression in respiratory cell populations.

To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.