Biology subjects

Sikkema, L.

Publications and source records attributed to Sikkema, L..

5 recordsLinked to original sources

bioRxiv2025

Automated evaluation of single-cell reference atlas mappings enables the identification of disease-associated cell states

The rise of single-cell atlases has opened up the use of reference atlases as a comprehensive healthy control for the study of disease. A key step for using atlases is the mapping of "query" disease or perturbed datasets onto a healthy reference atlas, which allows for comparison of the query to healthy controls and thereby the identification of perturbation-specific transcriptional cell states. However, the mapping success, i.e. the extent to which these mappings correctly reflect biological similarities and differences between query and reference, varies with e.g. experimental confounders in the data (dissociation protocol, single-cell assay) or the mapping method used, and cannot be predicted in advance. Moreover, an unsuccessful mapping can result in falsely reporting technical artifacts as disease or perturbation-specific cellular changes and in overlooking true altered states. Here, we present MapQC, a method that quantifies query-to-reference mapping success by leveraging the information present in the reference embedding. Specifically, MapQC tests the presence of remaining batch effect in the embedding after mapping by comparing inter-sample distances in the healthy reference itself to distance of query control samples to the reference. Similarly, it tests whether disease or perturbation-specific variation has been retained during mapping by testing whether query perturbed samples are more distant to the reference than reference samples are from each other. We apply MapQC to lung and endometrial query datasets, including data of idiopathic pulmonary fibrosis and Asherman Syndrome, mapped to large-scale healthy references. We show that MapQC correctly distinguishes successful mappings from unsuccessful ones where data visualization techniques such as UMAP can be deceiving, and that mapQC outperforms integration metrics often re-purposed for mapping qualitycontrol. Moreover, only mappings that MapQC identified as successful result in the correct identification of cell state changes specific to disease. Taken together, MapQC provides a critical quality-control metric for a more reliable, robust, and accurate use of reference atlases to study disease.

bioinformatics↗

bioRxiv2022

Single-cell reference mapping to construct and extend cell type hierarchies

Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups often using distinct terminology. Understanding how annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge. Whereas recent computational techniques, referred to as "reference mapping" methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e., queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present "treeArches", a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.

bioinformatics↗

bioRxiv2022

An integrated cell atlas of the human lung in health and disease

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

cell biology↗

bioRxiv2021

Dynamic structural cell responses in the thymus to acute injury, regeneration, and age

The thymus is essential for establishing adaptive immunity yet undergoes age-related atrophy leading to compromised immune responsiveness. The thymus is also extremely sensitive to acute insult and although capable of regeneration, this capacity declines with age. Focusing on non-hematopoietic stromal cells, and using single-cell and spatial transcriptomics, lineage-tracing, and advanced imaging, we discovered two atypical thymic epithelial cell (TEC) states that emerged with age. Age-associated (aa)TECs formed atypical high-density epithelial clusters that were devoid of thymocytes, an accretion of non-functional thymic tissue that worsened with age and exhibited features of partial epithelial-to-mesenchymal transition (EMT). In silico interaction analysis revealed that aaTEC emergence drew tonic signals from other TEC populations at baseline, acting as a sink for TEC growth factors. Following damage, aaTEC expanded substantially, further perturbing trophic pathways, and correlating with defective regeneration of the involuted thymus. These findings define a unique feature of thymic involution linked to immune aging.

immunology↗

bioRxiv2020

Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

bioinformatics↗