Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology.

Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the cellular dynamics of MS by modeling temporal and regional rates of disease progression in mouse experimental autoimmune encephalomyelitis (EAE). By performing single-cell spatial expression profiling using in situ sequencing (ISS), we annotated disease neighborhoods and found centrifugal evolution of active lesions. We demonstrated that disease-associated (DA)-glia arise independently of lesions and are dynamically induced and resolved over the disease course. Single-cell spatial mapping of human archival MS spinal cords confirmed the differential distribution of homeostatic and DA-glia, enabled deconvolution of active and inactive lesions into sub-compartments, and identified new lesion areas. By establishing a spatial resource of mouse and human MS neuropathology at a single-cell resolution, our study unveils the intricate cellular dynamics underlying MS.

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.

Spatial genomics maps the structure, nature and evolution of cancer clones.

Genome sequencing of cancers often reveals mosaics of different subclones present in the same tumour1-3. Although these are believed to arise according to the principles of somatic evolution, the exact spatial growth patterns and underlying mechanisms remain elusive4,5. Here, to address this need, we developed a workflow that generates detailed quantitative maps of genetic subclone composition across whole-tumour sections. These provide the basis for studying clonal growth patterns, and the histological characteristics, microanatomy and microenvironmental composition of each clone. The approach rests on whole-genome sequencing, followed by highly multiplexed base-specific in situ sequencing, single-cell resolved transcriptomics and dedicated algorithms to link these layers. Applying the base-specific in situ sequencing workflow to eight tissue sections from two multifocal primary breast cancers revealed intricate subclonal growth patterns that were validated by microdissection. In a case of ductal carcinoma in situ, polyclonal neoplastic expansions occurred at the macroscopic scale but segregated within microanatomical structures. Across the stages of ductal carcinoma in situ, invasive cancer and lymph node metastasis, subclone territories are shown to exhibit distinct transcriptional and histological features and cellular microenvironments. These results provide examples of the benefits afforded by spatial genomics for deciphering the mechanisms underlying cancer evolution and microenvironmental ecology.

BirthSeq, a new method to isolate and analyze dated cells in different vertebrates.

Embryonic development is a complex and dynamic process that unfolds over time and involves the production and diversification of increasing numbers of cells. The impact of developmental time on the formation of the central nervous system is well-documented, with evidence showing that time plays a critical role in establishing the identity of neuronal subtypes. However, the study of how time translates into genetic instructions driving cell fate is limited by the scarcity of suitable experimental tools. We introduce BirthSeq, a new method for isolating and analyzing cells based on their birth date. This innovative technique allows for in vivo labeling of cells, isolation via FACS, and analysis using high-throughput techniques. We tuned up BirthSeq in developmental organs across three vertebrate species (mouse, chick, and gecko), and fully made use of it for single-cell RNA sequencing and novel spatially resolved transcriptomic approaches in mouse and chick, respectively. Overall, BirthSeq provides a versatile tool for studying virtually any tissue in different vertebrate organism, helping to fill the necessity in developmental biology research by targeting cells and their temporal cues.

Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images.

Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with ×20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications.

Molecular architecture of the developing mouse brain.

The mammalian brain develops through a complex interplay of spatial cues generated by diffusible morphogens, cell-cell interactions and intrinsic genetic programs that result in probably more than a thousand distinct cell types. A complete understanding of this process requires a systematic characterization of cell states over the entire spatiotemporal range of brain development. The ability of single-cell RNA sequencing and spatial transcriptomics to reveal the molecular heterogeneity of complex tissues has therefore been particularly powerful in the nervous system. Previous studies have explored development in specific brain regions1-8, the whole adult brain9 and even entire embryos10. Here we report a comprehensive single-cell transcriptomic atlas of the embryonic mouse brain between gastrulation and birth. We identified almost eight hundred cellular states that describe a developmental program for the functional elements of the brain and its enclosing membranes, including the early neuroepithelium, region-specific secondary organizers, and both neurogenic and gliogenic progenitors. We also used in situ mRNA sequencing to map the spatial expression patterns of key developmental genes. Integrating the in situ data with our single-cell clusters revealed the precise spatial organization of neural progenitors during the patterning of the nervous system.

A roadmap for the Human Developmental Cell Atlas.

The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.

Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications.

Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.

A drained nutrient-poor peatland forest in boreal Sweden constitutes a net carbon sink after integrating terrestrial and aquatic fluxes.

Northern peatlands provide a globally important carbon (C) store. Since the beginning of the 20th century, however, large areas of natural peatlands have been drained for biomass production across Fennoscandia. Today, drained peatland forests constitute a common feature of the managed boreal landscape, yet their ecosystem C balance and associated climate impact are not well understood, particularly within the nutrient-poor boreal region. In this study, we estimated the net ecosystem carbon balance (NECB) from a nutrient-poor drained peatland forest and an adjacent natural mire in northern Sweden by integrating terrestrial carbon dioxide (CO2 ) and methane (CH4 ) fluxes with aquatic losses of dissolved organic C (DOC) and inorganic C based on eddy covariance and stream discharge measurements, respectively, over two hydrological years. Since the forest included a dense spruce-birch area and a sparse pine area, we were able to further evaluate the effect of contrasting forest structure on the NECB and component fluxes. We found that the drained peatland forest was a net C sink with a 2-year mean NECB of -115 ± 5 g C m-2 year-1 while the adjacent mire was close to C neutral with 14.6 ± 1.7 g C m-2 year-1 . The NECB of the drained peatland forest was dominated by the net CO2 exchange (net ecosystem exchange [NEE]), whereas NEE and DOC export fluxes contributed equally to the mire NECB. We further found that the C sink strength in the sparse pine forest area (-153 ± 8 g C m-2 year-1 ) was about 1.5 times as high as in the dense spruce-birch forest area (-95 ± 8 g C m-2 year-1 ) due to enhanced C uptake by ground vegetation and lower DOC export. Our study suggests that historically drained peatland forests in nutrient-poor boreal regions may provide a significant net ecosystem C sink and associated climate benefits.

Associations between food intake and psychosomatic symptoms in 16-year-old adolescents.

AIMS: An increase in psychosomatic symptoms among adolescents has recently been reported. Few studies have examined the relation between food intake and psychosomatic symptoms. The aim was to study the association between food intake and overall psychosomatic burden and separate psychosomatic symptoms. METHODS: In this cross-sectional study, we used data from 6248 girls and 7153 boys in south-east Sweden who turned 16 years of age during the academic years 2009/2010 to 2015/2016 and responded to a health questionnaire at the school health services. The association between overall healthy food intake and a low psychosomatic burden was calculated as odds ratios (95% confidence interval) and stratified for other lifestyle habits and gender. RESULTS: Sixty-nine per cent of the boys and 35% of the girls had a low psychosomatic burden. There was a positive association between an overall healthy food intake and a low psychosomatic burden (P<0.0001), regardless of other lifestyle habits and gender. An overall healthy food intake was also positively associated with a lower frequency of the separate psychosomatic symptoms of concentration difficulties, sleep difficulties, a poor appetite or dizziness (P<0.0001). CONCLUSIONS: A healthy food intake seems to be associated with a low psychosomatic burden among adolescents. Further knowledge is needed to explore whether an improved food intake can reduce psychosomatic symptoms and enhance mental health.

Circulating exosome-like vesicle and skeletal muscle microRNAs are altered with age and resistance training.

The age-related loss of skeletal muscle mass and functionality, known as sarcopenia, is a critical risk factor for morbidity and all-cause mortality. Resistance exercise training (RET) is the primary countermeasure to fight sarcopenia and ageing. Altered intercellular communication is a hallmark of ageing, which is not well elucidated. Circulating extracellular vesicles (EVs), including exosomes, contribute to intercellular communication by delivering microRNAs (miRNAs), which modulate post-translational modifications, and have been shown to be released following exercise. There is little evidence regarding how EVs or EV-miRNAs are altered with age or RET. Therefore, we sought to characterize circulating EVs in young and older individuals, prior to and following a 12-week resistance exercise programme. Plasma EVs were isolated using size exclusion chromatography and ultracentrifugation. We found that ageing reduced circulating expression markers of CD9, and CD81. Using late-passage human myotubes as a model for ageing in vitro, we show significantly lower secreted exosome-like vesicles (ELVs). Further, levels of circulating ELV-miRNAs associated with muscle health were lower in older individuals at baseline but increased following RET to levels comparable to young. Muscle biopsies show similar age-related reductions in miRNA expressions, with largely no effect of training. This is reflected in vitro, where aged myotubes show significantly reduced expression of endogenous and secreted muscle-specific miRNAs (myomiRs). Lastly, proteins associated with ELV and miRNA biogenesis were significantly higher in both older skeletal muscle tissues and aged human myotubes. Together we show that ageing significantly affects ELV and miRNA cargo biogenesis, and release. RET can partially normalize this altered intercellular communication. KEY POINTS: We show that ageing reduces circulating expression of exosome-like vesicle (ELV) markers, CD9 and CD81. Using late-passage human skeletal myotubes as a model of ageing, we show that secreted ELV markers are significantly reduced in vitro. We find circulating ELV miRNAs associated with skeletal muscle health are lower in older individuals but can increase following resistance exercise training (RET). In skeletal muscle, we find altered expression of miRNAs in older individuals, with no effect of RET. Late-passage myotubes also appear to have aberrant production of endogenous myomiRs with lower abundance than youthful counterparts In older skeletal muscle and late-passage myotubes, proteins involved with ELV- and miRNA biogenesis are upregulated.

Highly multiplexed targeted sequencing strategy for infectious disease surveillance.

BACKGROUND: Global efforts to characterize diseases of poverty are hampered by lack of affordable and comprehensive detection platforms, resulting in suboptimal allocation of health care resources and inefficient disease control. Next generation sequencing (NGS) can provide accurate data and high throughput. However, shotgun and metagenome-based NGS approaches are limited by low concentrations of microbial DNA in clinical samples, requirements for tailored sample and library preparations plus extensive bioinformatics analysis. Here, we adapted molecular inversion probes (MIPs) as a cost-effective target enrichment approach to characterize microbial infections from blood samples using short-read sequencing. We designed a probe panel targeting 2 bacterial genera, 21 bacterial and 6 fungi species and 7 antimicrobial resistance markers (AMRs). RESULTS: Our approach proved to be highly specific to detect down to 1 in a 1000 pathogen DNA targets contained in host DNA. Additionally, we were able to accurately survey pathogens and AMRs in 20 out of 24 samples previously profiled with routine blood culture for sepsis. CONCLUSIONS: Overall, our targeted assay identifies microbial pathogens and AMRs with high specificity at high throughput, without the need for extensive sample preparation or bioinformatics analysis, simplifying its application for characterization and surveillance of infectious diseases in medium- to low- resource settings.

Probabilistic cell typing enables fine mapping of closely related cell types in situ.

Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.

Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse.

BACKGROUND: Opting for or against the administration of adjuvant chemotherapy in therapeutic management of stage II colon cancer remains challenging. Several studies report few survival benefits for patients treated with adjuvant therapy and additionally revealing potential side effects of overtreatment, including unnecessary exposure to chemotherapy-induced toxicities and reduced quality of life. Predictive biomarkers are urgently needed. We, therefore, hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers. METHODS: The spatial tissue composition of stage II colon cancer patients was examined by a novel spatial transcriptomics technology with sub-cellular resolution, namely in situ sequencing. A panel of 176 genes investigating specific cancer-associated processes such as apoptosis, proliferation, angiogenesis, stemness, oxidative stress, hypoxia, invasion and components of the tumour microenvironment was designed to examine differentially expressed genes in tissue of relapsed versus non-relapsed patients. Therefore, FFPE slides of 10 colon cancer stage II patients either classified as relapsed (5 patients) or non-relapsed (5 patients) were in situ sequenced and computationally analysed. RESULTS: We identified a tumour gene signature that enables the subclassification of tissue into neoplastic and non-neoplastic compartments based on spatial expression patterns obtained through in situ sequencing. We developed a computational tool called Genes-To-Count (GTC), which automates the quantification of in situ signals, accurately mapping their position onto the spatial tissue map and automatically identifies neoplastic and non-neoplastic tissue compartments. The GTC tool was used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer. CONCLUSIONS: In depth spatial in situ sequencing showed potential to provide a deeper understanding of the underlying mechanisms involved in the recurrence of disease and revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our open-access GTC-tool allowed us to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.

Landscape-variability of the carbon balance across managed boreal forests.

Boreal forests are important global carbon (C) sinks and, therefore, considered as a key element in climate change mitigation policies. However, their actual C sink strength is uncertain and under debate, particularly for the actively managed forests in the boreal regions of Fennoscandia. In this study, we use an extensive set of biometric- and chamber-based C flux data collected in 50 forest stands (ranging from 5 to 211 years) over 3 years (2016-2018) with the aim to explore the variations of the annual net ecosystem production (NEP; i.e., the ecosystem C balance) across a 68 km2 managed boreal forest landscape in northern Sweden. Our results demonstrate that net primary production rather than heterotrophic respiration regulated the spatio-temporal variations of NEP across the heterogeneous mosaic of the managed boreal forest landscape. We further find divergent successional patterns of NEP in our managed forests relative to naturally regenerating boreal forests, including (i) a fast recovery of the C sink function within the first decade after harvest due to the rapid establishment of a productive understory layer and (ii) a sustained C sink in old stands (131-211 years). We estimate that the rotation period for optimum C sequestration extends to 138 years, which over multiple rotations results in a long-term C sequestration rate of 86.5 t C ha-1 per rotation. Our study highlights the potential of forest management to maximize C sequestration of boreal forest landscapes and associate climate change mitigation effects by developing strategies that optimize tree biomass production rather than heterotrophic soil C emissions.

On the uncertainty in estimates of the carbon balance recovery time after forest clear-cutting.

An essential metric for describing carbon dynamics in managed forest landscapes is the recovery time of the carbon balance after clear-cutting. Here, we demonstrate how the age-dependent mathematical trajectory is affected by both the selected model and data availability, leading to considerable uncertainty in the modelling of the net ecosystem production (NEP) over stand age. We further show that the initial carbon loss estimates associated with the timing of the source-sink transition (SST) are significant, but may have a limited effect on the total carbon sequestration at the end of the standard (RP, 120 years) or optimal (OCS) rotation periods.

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions.

Wetlands are the largest natural source of methane (CH4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.

SCRINSHOT enables spatial mapping of cell states in tissue sections with single-cell resolution.

Changes in cell identities and positions underlie tissue development and disease progression. Although single-cell mRNA sequencing (scRNA-Seq) methods rapidly generate extensive lists of cell states, spatially resolved single-cell mapping presents a challenging task. We developed SCRINSHOT (Single-Cell Resolution IN Situ Hybridization On Tissues), a sensitive, multiplex RNA mapping approach. Direct hybridization of padlock probes on mRNA is followed by circularization with SplintR ligase and rolling circle amplification (RCA) of the hybridized padlock probes. Sequential detection of RCA-products using fluorophore-labeled oligonucleotides profiles thousands of cells in tissue sections. We evaluated SCRINSHOT specificity and sensitivity on murine and human organs. SCRINSHOT quantification of marker gene expression shows high correlation with published scRNA-Seq data over a broad range of gene expression levels. We demonstrate the utility of SCRINSHOT by mapping the locations of abundant and rare cell types along the murine airways. The amenability, multiplexity, and quantitative qualities of SCRINSHOT facilitate single-cell mRNA profiling of cell-state alterations in tissues under a variety of native and experimental conditions.

De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks.

With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.

Factors related to COVID-19 mortality among three Swedish intensive care units-A retrospective study.

BACKGROUND: Mortality due to acute hypoxemic respiratory failure (AHRF) in patients with coronavirus disease-19 (COVID-19) differs across units, regions, and countries. These variations may be attributed to several factors, including comorbidities, acute physiological derangement, disease severity, treatment, ethnicity, healthcare system strain, and socioeconomic status. This study aimed to explore the features of patient characteristics, clinical management, and staffing that may be related to mortality among three intensive care units (ICUs) within the same hospital system in South Sweden. METHODS: We retrospectively analyzed ICU patients with COVID-19 and AHRF in Region Jönköping County, Sweden. The primary outcome was the 90-day mortality rate. We used univariate and multivariable logistic regression analyses to investigate the relationship of predictors with outcomes. RESULTS: Between March 15, 2020, and May 31, 2021, 331 patients with AHRF and COVID-19 were admitted to the three ICUs. There were differences in disease severity, treatments, process-related factors, and socioeconomic factors between the units. These factors were related to 90-day mortality. After multivariable adjustment, age, severity of acute respiratory distress syndrome, and the number of nurses per ICU-bed independently predicted 90-day mortality. CONCLUSION: Age, disease severity, and nurse staffing, but not treatment or socioeconomic status, were independently associated with 90-day mortality among critically ill patients with AHRF due to COVID-19. We also identified variations in care related processes, which may be a modifiable risk factor and warrants future investigation.