Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.10.15.618506

The First Comprehensive Description of the Platelet Single Cell Transcriptome

Abstract

Platelets are derived from megakaryocytes, either in peripheral or pulmonic circulation. The transcriptome of megakaryocytes has been studied, while the platelet transcriptome is thought to be a reflection of their parent cells; it has not yet been investigated. Although platelets lack nuclei, they inherit RNA from their parent megakaryocytes, while only about 10% of them are believed to contain enough RNA for meaningful analysis. This study explores the potential of single-cell RNA sequencing to analyze the platelet transcriptome, aiming to expand our understanding of platelets beyond their traditional role in coagulation. Using acridine orange staining and antibody-based sequencing, we successfully sequenced RNA from seven healthy donors. Results revealed significant heterogeneity in gene expression, with common platelet markers, such as ITGA2B and GP1B, being less abundant than expected. Interestingly, immune markers associated with lung megakaryocytes were not strongly represented in peripheral platelets. Comparison with current algorithms for cell identification suggests that platelets are often misclassified as other blood cell types, highlighting limitations of existing pipelines in platelet annotation. This misclassification may have led to misrepresentation of platelet transcriptomics in previous studies. These findings underscore the need for tailored sequencing methods to accurately profile platelets and set the foundation for further exploration of platelet biology and immune function, potentially opening avenues for therapeutic interventions in immune modulation, drug delivery, and the use of platelets as disease biomarkers in cancer and other conditions. Key PointsPlatelet single cell sequencing can be implemented with appropriate technical refinements to ensure optimal isolation without exogenous activation. In comparison to bulk sequencing techniques, single cell analysis affords the ability to exclude contaminating cells enabling examination of the authentic platelet transcriptome. This is critically important as contaminating cells contain far more RNA ultimately skewing results of transcriptomic analysis. Most platelets do not contain significant levels of commonly expected transcripts such as ITGA2B, GP1B, TREML1. In the context of recent data, our single cell transcriptomic data supports the intradividual and interindividual heterogeneity of the platelet transcriptome. The lung megakaryocyte signature is not disguisable in peripheral platelets. Further studies are needed to understand sources of RNA within platelets and the impact of the platelet microenvironment. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/618506v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@556339org.highwire.dtl.DTLVardef@1e543c4org.highwire.dtl.DTLVardef@c41cb5org.highwire.dtl.DTLVardef@138d779_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOVisual abstractC_FLOATNO C_FIG

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wolfsberger, W., Dietz, C., Foster, C., Oleksyk, T. K., Washington, V., Lynch, D.. 2024-10-18. The First Comprehensive Description of the Platelet Single Cell Transcriptome. https://doi.org/10.1101/2024.10.15.618506

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Comparative functional genomics reveals distributed SERPINE1 like characteristics among Drosophila serpins

Serpins (serine protease inhibitors) regulate proteolytic activity through a suicide-substrate inhibitory mechanism, whereby the serpin covalently traps and inactivates their target protease. Serpins are broadly conserved across eukaryotes, but the evolutionary and functional relationships between human SERPINE1, and Drosophila Serpins in incompletely defined. To identify candidate Drosophila functional analogs of SERPINE1/PAI-1, a multi-omics framework was applied to thoroughly compare the serpins across species. The multi-level analysis integrated orthology prediction, protein sequence and structural characterization, phylogenetic reconstruction, bulk tissue transcriptomics, single-cell transcriptomics, and aging-associated expression analysis. Candidate serpins (Spn42Da, Spn42Db, Spn42Dc, Spn42Dd, Spn28B, and Spn28F) were evaluated using DIOPT, AlphaFold/ChimeraX structural comparisons, reactive center loop analyses, phylogenetic distances, GTEx, Human Protein Atlas, FlyAtlas2, Fly Cell Atlas, and Aging Fly Cell Atlas datasets. Comparative analyses demonstrated full-length sequence and reactive center loop (RCL) divergence despite preservation of the canonical serpin fold. Tissue, cell-type, and age-associated expression analysis revealed distinct candidate-specific patterns. Spn42Dd comparisons uncovered similarities to human SERPINE1 in sequence/structural and broad expression but did not represent the strongest overall cross-species expression correlations. Spn42Da and Spn42Db received the highest cross species integrated support. Quality check of the ranking confirmed that Spn42Da and Spn42Db were stable top candidates across orthology, sequence alignment, and expression domains. The multi-domain analysis was unable to identify a single definitive Drosophila serpin as the closest counterpart to human SERPINE1, indicating that cross-species similarity is distributed across multiple candidate serpins. This study therefore provides a quantitative method for prioritizing potential evolutionary and functional analogs across highly divergent species.

bioinformatics↗

CellChat Hotspot: A Targeted Approach to Cell Cell Communication Analysis With Spatial transcriptomics Validation.

Abstract. In recent years, single cell RNA sequencing (scRNA seq) has enabled the investigation of intercellular communication through Cell Cell Communication (CCC) tools such as CellChat. Despite their potential, these approaches remain challenging to interpret, particularly when global analyses generate complex interaction networks that may include biologically irrelevant signals. To address this limitation, we developed a framework termed CellChat Hotspot, designed to improve the biological interpretability of inferred communication networks. This approach integrates gene set enrichment analysis with a context driven filtering strategy, selecting both genes and cells based on their functional relevance prior to communication inference. Applied to stage II colon cancer data, CellChat Hotspot enabled the identification of condition-specific interaction patterns associated with recurrence. Non recurrent patients showed enrichment of immune related processes, whereas recurrent cases were characterized by pathways associated with invasion and metastasis. Notably, a loss of interaction in the HSPG pathway between CALB1+ and stromal cells was observed in recurrent patients, suggesting a potential disruption of cell matrix adhesion mechanisms. To verify the reliability of this finding, we applied the framework to spatial transcriptomics data, which confirmed the presence of the HSPG2 DAG1 interaction in non-recurrent tumors and its near absence in recurrent ones. Overall, this framework provides a biologically guided strategy for studying intercellular communication in complex systems such as the tumor microenvironment.

bioinformatics↗

DiffTernary: a Diffusion Model for Molecular Glue Ternary Complex Docking

Molecular glues (MGs) modulate protein function by inducing or stabilizing protein interactions. However, accurately modeling MG ternary complexes remains challenging. Here, we present DiffTernary, a diffusion-based docking method for the joint prediction of relative protein arrangement and MG binding pose. We introduce Ternary-PDB, a dataset of 76,702 experimentally determined ternary complexes curated from the Protein Data Bank using structural filters and a release cutoff aligned with AlphaFold 3 (AF3). When trained on this dataset, DiffTernary achieved acceptable protein-protein interaction (PPI) predictions (DockQ > 0.23) for 50.0% of MGBench complexes with a single prediction, compared with 51.1% for AF3. In comparisons with AF3, OpenFold3, ESMFold2, Protenix-v1 and Protenix-v2, DiffTernary ranked second with a best-of-25 PPI prediction success rate of 78.4%, behind ESMFold2 (90.9%) and ahead of AF3 (72.7%). A separate DiffTernary model trained on TernaryDB achieved a mean DockQ of 0.306 on the 94 complexes in the TernaryDB MGD test set with a single prediction, compared with 0.228 for DeepTernary. Confidence-based selection among 40 samples yielded acceptable PPI predictions for 41.5% of complexes, compared with 30.9% for DeepTernary. An E2Former variant increased training and sampling throughput 6.90-fold and 4.37-fold, respectively, under the evaluated configurations. Together, DiffTernary and Ternary-PDB provide an efficient docking approach for MG complex prediction and a valuable structural resource for MG discovery and design.

bioinformatics↗