A reference tissue atlas for the human kidney
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Citations
How to Get Started with Single Cell RNA Sequencing Data Analysis.
Perspectives in systems nephrology.
An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue
References
Lipid compartmentalization in the endosome system
Lysinuric protein intolerance: mechanisms of pathophysiology.
New insights into the role of iron in inflammation and atherosclerosis
Tight Junctions as Targets and Effectors of Mucosal Immune Homeostasis.
Hijacking solute carriers for proton-coupled drug transport.
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Frequently Asked Questions (15)
Q2. What are the future works mentioned in the paper "Towards building a smart kidney atlas: network-based integration of multimodal transcriptomic, proteomic, metabolomic and imaging data in the kidney precision medicine project" ?
Their approach is amendable to future computational modeling studies that can further improve the proposed tissue atlas. In addition to the integrated analytics presented here, the KPMP is also building a community-based Kidney Tissue Atlas Ontology ( KTAO ), which will systematically integrate different types information ( such as clinical, pathological, cell and molecular ) into a logically defined tissue atlas, which can then be further utilized to support various applications 34.
Q3. What was used for the data normalization and scaling?
‘SCTransform’ was used for data normalization and scaling (based on top 2,000 features), followed by principal component analysis.
Q4. What is the role of fatty acid oxidation in tubulointerstitial ?
Decrease in fatty acid oxidation, resulting in a loss of ATP generation, has been shown to be a significant contributor to tubulointerstitial fibrosis 19.
Q5. How many libraries were needed to reidentify podocytes?
On average 12 and 15 libraries (~3,100 and 3,835 nuclei) allowed reidentification of seven of the top 10 predicted podocyte and proximal tubule MBCO SCPs, respectively, while 21 libraries (~5,462 nuclei) were sufficient to reidentify five of the topwas not certified by peer review) is the author/funder.
Q6. How many jensenlab confidences were used to identify each gene?
Subcellular localization of each gene was identified using the jensenlab human compartment database based on a jensenlab confidence of at least four (i.e. 80% of maximum confidence in the database) 28.
Q7. What are the metabolites of the energy carrier ATP?
Tubulointerstitial metabolites, for example, contain glucose, cofactors of the pyruvate dehydrogenase complex and multiple adenosine nucleotides/nucleosides (i.e. metabolites of the energy carrier ATP).
Q8. How many libraries are needed for a consistent detection of podocytes?
Their results indicate that for a consistent detection of podocytes (i.e. in more than 95% of all down sampled datasets with the same library counts), at least 16 (~11,727 cells) or 7 libraries (1,837 nuclei) are needed if subjected to single-cell RNASeq (Figure 4A) or single-nucleus RNASeq (Figure 4B), respectively.
Q9. How many differentially expressed genes and proteins were predicted by each assay?
Top 300 differentially expressed genes (DEGs) and proteins (DEPs) predicted by each assay for each analyzed cell type/tissue subsegment.
Q10. How many SCPs can be included in the top seven predictions?
Notice that the top seven predictions based on dynamic enrichment analysis can contain more than seven SCPs, since each prediction is either a single SCP or a unique combination of two or three SCPs.
Q11. How many samples were sufficient to reproduce the results for the full dataset?
For the LMD proteomics dataset, six to eight samples were sufficient to reproduce the results obtained for the full datasets with only minor variations in the correlation of identified DEGs (Figure 4C) and SCPs (Supplementary Figure 2E) or SCP rankings (Figure 4C).
Q12. What is the way to integrate the three different assays?
An idealized integration scenario would combine these assays synergistically such that they could complement the shortcomings of each other, improve quality control metrics across technologies, and increase rigor and reproducibility of the overall study.
Q13. How many predictions are needed to re-identify the top 10 or seven predictions?
the authors determined how many SCPs have to be considered in a down-sampled analysis to re-identify at least 70% (or 50%) of the top 10 or seven predictions obtained from standard or dynamic enrichment analysis with the full dataset, respectively.
Q14. What is the role of the collecting duct in regulating systemic electrolyte and?
Principal cell/collecting duct networks concentrate on ion reabsorption (Supplementary Figure 1C), emphasizing the important role of the collecting duct in fine-tuning these mechanisms, thereby regulating systemic electrolyte and water balance.
Q15. What is the correlation between the gene expression profiles of cells and LCM segments?
To compute the Pearson correlation between the gene expression profiles of cells and LCM segments, the gene profiles were restricted to genes shared between the two datasets and showing variable expression in the single-cell dataset and correlations were computed between the logarithm of the mean ratio vector for each LCM segment and the scaled expression profile of each cell in the single cell dataset.