Biology subjects

Czarnewski, P.

Publications and source records attributed to Czarnewski, P..

10 recordsLinked to original sources

bioRxiv2024

Spatial Dynamics of the Developing Human Heart

Heart development relies on a topologically defined interplay between a diverse array of cardiac cells. We finely curated spatial and single-cell measurements with subcellular imaging-based transcriptomics validation to explore spatial dynamics during early human cardiogenesis. Analyzing almost 80,000 individual cells and 70,000 spatially barcoded tissue regions between the 5.5th and 14th postconceptional weeks, we identified 31 coarse- and 72 fine-grained cell states and mapped them to highly resolved cardiac cellular niches. We provide novel insight into the development of the cardiac pacemaker-conduction system, heart valves, and atrial septum, and decipher heterogeneity of the hitherto elusive cardiac fibroblast population. Furthermore, we describe the formation of cardiac autonomic innervation and present the first spatial account of chromaffin cells in the fetal human heart. We support independent exploration of our datasets by an open-access, spatially centric interactive viewer. In summary, our study delineates the cellular and molecular landscape of the developing hearts architecture, offering links to genetic causes of heart disease.

developmental biology↗

bioRxiv2024

High-parametric protein maps reveal the spatial organization in early-developing human lung

The respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lungs and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.

developmental biology↗

bioRxiv2023

Spatial landmark detection and tissue registration with deep learning

Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle non-linear deformations between tissue sections, and are ineffective for z-stack alignment, other modalities beyond image data, or multimodal data. We address these challenges by introducing a new landmark detection and registration method, utilizing neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets, demonstrating superior performance in both accuracy and stability compared to existing approaches.

bioinformatics↗

bioRxiv2023

Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10X Genomics capable of mapping hundreds of transcripts in situ at a subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore eight preview Xenium datasets of the mouse brain and two of human breast cancer by comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies. In addition, we benchmarked the performance of multiple open source computational tools when applied to Xenium datasets in tasks including cell segmentation, segmentation-free analysis, selection of spatially variable genes and domain identification, among others. This study serves as the first independent analysis of the performance of Xenium, and provides best-practices and recommendations for analysis of such datasets.

bioinformatics↗

bioRxiv2023

Spatial Multimodal Analysis of Transcriptomes and Metabolomes in Tissues

We present a spatial omics approach that merges and expands the capabilities of independently performed in situ assays on a single tissue section. Our spatial multimodal analysis combines histology, mass spectrometry imaging, and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular weight metabolites across tissue regions. We demonstrate the potential of our method using murine and human brain samples in the context of dopamine and Parkinsons disease.

genomics↗

bioRxiv2022

Molecular and spatial design of early skin development

A wealth of specialized cell populations within the skin facilitates its hair producing, protective, sensory and thermoregulatory functions, but how the vast cell-type diversity and tissue architecture develops is largely unexplored. Here, with single-cell transcriptomics, spatial cell-type assignment and cell-lineage tracing we deconstruct early embryonic mouse skin during the key transitions from seemingly uniform developmental precursor states to a multilayered, multilineage epithelium and complex dermal identity. We reveal the spatiotemporal emergence of hair-follicle-inducing, muscle-supportive, and fascia-forming fibroblasts. We also uncover the formation of the panniculus carnosus muscle, sprouting blood vessels without pericyte coverage, and the earliest residence of mast and dendritic immune cells in skin. Finally, we reveal an unexpected epithelial heterogeneity within the early single-layered epidermis and a signaling-rich periderm layer. Overall, this cellular blueprint of early skin development establishes histological landmarks - essential for placing cells in their spatial tissue context - and highlights unprecedented dynamical interactions among skin cells.

developmental biology↗

bioRxiv2022

Decoding spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin

The human spinal cord contains diverse cell types, governed by a series of spatiotemporal events for tissue assembly and functions. However, the spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. Single-cell RNA sequencing and spatial transcriptomics techniques have advanced the understanding of human organ development considerably. By performing integrated analysis of single-cell and spatial multi-omics methods, we created a comprehensive developmental cell atlas of the first trimester human spinal cord. Our data revealed that the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. Beyond this resource, we unexpectedly discovered unique events in human spinal cord development compared to rodents, including earlier quiescence of active neural stem cells, different regulation of stem cell differentiation, and distinct spatiotemporal genetic regulations of cell fate choices. In addition, using our atlas we identified specific gene expression in cancer stem cells in ependymomas. Thus, we demonstrate spatiotemporal genetic regulation of human spinal cord development as well as its potential to understand novel disease mechanisms and to inspire new therapies.

developmental biology↗

bioRxiv2021

A Landmark-based Common Coordinate Framework for Spatial Transcriptomics Data

1The increasing amount of spatial transcriptomics data prompts for means to amalgamate observations from distinct experiments, especially attractive is to cast quantities from different sources into a common coordinate frame-work (CCF) to relate signals across space. We here present a method that enables transfer of information from multiple samples to a reference representing a CCF, and show its utility by analyzing an assortment of real and synthetic data sets.

bioinformatics↗

bioRxiv2020

Single cell BCR and transcriptome analysis after respiratory virus infection reveals spatiotemporal dynamics of antigen-specific B cell responses.

B cell responses are a critical component of anti-viral immunity. However, a comprehensive picture of antigen-specific B cell responses, differentiation, clonal proliferation and dynamics in different organs after infection is lacking. Here, we combined single-cell RNA sequencing with single-cell B cell receptor (BCR) characterization of antigen-specific cells in the draining lymph nodes, spleen and lungs after influenza infection. We identify several novel B cell subpopulations forming after infection and find organ-specific differences that persist over the course of the response. We discover important transcriptional differences between memory cells in lungs and lymphoid organs and describe organ-restricted clonal expansion. Strikingly, by combining BCR mutational analysis, monoclonal antibody expression and affinity measurements we find no differences between germinal center (GC)-derived memory and plasmacells, at odds with an affinity-based selection model. By linking antigen-recognition with transcriptional programming, clonal-proliferation and differentiation, these finding provide important advances in our understanding of antiviral B cell immunity.

immunology↗

bioRxiv2019

Conserved transcriptomic profile between mouse and human colitis allows temporal dynamic visualization of IBD-risk genes and unsupervised patient stratification

AbstrasctDespite the fact that ulcerative colitis (UC) patients show heterogeneous clinical manifestation and diverse response to biological therapies, all UC patients are classified as one group. Therefore, there is a lack of tailored therapies. In order to design these, an unsupervised molecular re-classification of UC patients is evoked. Classical clustering approaches based on tissue transcriptomic data were not able to classify UC patients into subgroups, likely due to associated covariates. In addition, while genome wide association studies (GWAS) have identified potential new target genes, their temporal dynamic revealing the optimal therapeutic window of time remains to be elucidated. To overcome the limitations, we generated time-series transcriptome data from a mouse model of colitis, which was then cross-compared with human datasets. This allowed us to visualize IBD-risk gene expression kinetics and reveal that the expression of the majority of IBD-risk genes peak during the inflammatory phase, and not the recovery phase. Moreover, by restricting the analysis to the most differentially expressed genes shared between mouse and human, we were able to cluster UC patients into two subgroups, termed UC1 and UC2. We found that UC1 patients expressed higher copy of genes involved in neutrophil recruitment, activation and degranulation compared to UC2. Of note, we found that over 87% of UC1 patients failed to respond to two of the most widely-used biological therapies for UC.\n\nThis study serves as a proof of concept that cross-species comparison of gene expression profiles enables the temporal annotation of disease-associated gene expression and the stratification of patients as of yet considered molecularly undistinguishable.

immunology↗