Automated Author ProfileD. McManus, David
D. McManus, David
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.1 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Transcripts in platelets are largely produced in precursor megakaryocytes but remain physiologically active as platelets translate RNAs and regulate protein/RNA levels. Recent studies using transcriptome sequencing (RNA-seq) characterized the platelet transcriptome in limited number of non-diseased individuals. Here, we expand upon these RNA-seq studies by completing RNA-seq in platelets from 32 patients with acute myocardial infarction (MI). Our goals were to characterize the platelet transcriptome using a population of patients with acute MI and relate gene expression to platelet aggregation measures and ST-segment elevation MI (STEMI) (n = 16) vs. non-STEMI (NSTEMI) (n = 16) subtypes. Similar to other studies, we detected 9565 expressed transcripts, including several known platelet-enriched markers (e.g. PPBP, OST4). Our RNA-seq data strongly correlated with independently ascertained platelet expression data and showed enrichment for platelet-related pathways (e.g. wound response, hemostasis, and platelet activation), as well as actin-related and post-transcriptional processes. Several transcripts displayed suggestively higher (FBXL4, ECHDC3, KCNE1, TAOK2, AURKB, ERG, and FKBP5) and lower (MIAT, PVRL3, and PZP) expression in STEMI platelets compared to NSTEMI. We also identified transcripts correlated with platelet aggregation to TRAP (ATP6V1G2, SLC2A3), collagen (CEACAM1, ITGA2), and ADP (PDGFB, PDGFC, ST3GAL6). Our study adds to current platelet gene expression resources by providing transcriptome-wide analyses in platelets isolated from patients with acute MI. In concert with prior studies, we identify various genes for further study in regards to platelet function and acute MI. Future platelet RNA-seq studies examining more diverse sets of healthy and diseased samples will add to our understanding of platelet thrombotic and non-thrombotic functions.
Authors
- D. Eicher, John ;
- Wakabayashi, Yoshiyuki ;
- Vitseva, Olga ;
- Esa, Nada ;
- Yang, Yanqin ;
- Zhu, Jun ;
- E. Freedman, Jane ;
- D. McManus, David ;
- D. Johnson, Andrew
Transcripts in platelets are largely produced in precursor megakaryocytes but remain physiologically active as platelets translate RNAs and regulate protein/RNA levels. Recent studies using transcriptome sequencing (RNA-seq) characterized the platelet transcriptome in limited number of non-diseased individuals. Here, we expand upon these RNA-seq studies by completing RNA-seq in platelets from 32 patients with acute myocardial infarction (MI). Our goals were to characterize the platelet transcriptome using a population of patients with acute MI and relate gene expression to platelet aggregation measures and ST-segment elevation MI (STEMI) (n = 16) vs. non-STEMI (NSTEMI) (n = 16) subtypes. Similar to other studies, we detected 9565 expressed transcripts, including several known platelet-enriched markers (e.g. PPBP, OST4). Our RNA-seq data strongly correlated with independently ascertained platelet expression data and showed enrichment for platelet-related pathways (e.g. wound response, hemostasis, and platelet activation), as well as actin-related and post-transcriptional processes. Several transcripts displayed suggestively higher (FBXL4, ECHDC3, KCNE1, TAOK2, AURKB, ERG, and FKBP5) and lower (MIAT, PVRL3, and PZP) expression in STEMI platelets compared to NSTEMI. We also identified transcripts correlated with platelet aggregation to TRAP (ATP6V1G2, SLC2A3), collagen (CEACAM1, ITGA2), and ADP (PDGFB, PDGFC, ST3GAL6). Our study adds to current platelet gene expression resources by providing transcriptome-wide analyses in platelets isolated from patients with acute MI. In concert with prior studies, we identify various genes for further study in regards to platelet function and acute MI. Future platelet RNA-seq studies examining more diverse sets of healthy and diseased samples will add to our understanding of platelet thrombotic and non-thrombotic functions.
Authors
- D. Eicher, John ;
- Wakabayashi, Yoshiyuki ;
- Vitseva, Olga ;
- Esa, Nada ;
- Yang, Yanqin ;
- Zhu, Jun ;
- E. Freedman, Jane ;
- D. McManus, David ;
- D. Johnson, Andrew