Additional file 9: of High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis

Table S8. Detailed annotation on assembled full-length transcripts for antibody heavy and light chains in all identified B cells. (CSV 8 kb

Additional file 2: of High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis

Table S1. Differentially expressed genes in the putative cluster (cluster 8). (CSV 1 kb

Additional file 1: of High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis

Figure S1. Overall viability and abundance of Cd45+ cells across different durations of preservation time. At the bottom of each panel, the percentage in white was calculated as: counts of PI- events / counts of all events for the top panels, and counts of Cd45+ events / counts of all events for the bottom panels. Raw counts were shown in the fractions. Figure S2. cDNA concentration and smearing assessed via fragment analysis for single Cd45+ cells from mouse kidneys after different durations of preservation presented as (A) electrophoresis traces and (B) gel image. Figure S3. Genebody coverage of Cd45+ single cells from mouse kidneys after different durations of time. (A) 5′-3′ read coverage on exons. (B) Distribution of skewness of 5′-3′ read coverage on exons. Figure S4. Identification of cell types in Cd45+ single cells from mouse kidneys. (A) Number of genes detected cast on 2d tSNE. (B) Uniquely expressing genes identified for each putative cell clusters. (Coloring of cluster ID follows that in Fig. 3A.) Figure S5. genes rejected by null hypothesis (DE genes) at FDR = 0.05 between fresh and preserved tissue in cluster 2, 4, 6, 7. (A) Number of DE genes identified between each of the eight identified cell types and its nearest neighbor (defined in Fig. 3A) with incrementing FDR. (B) Volcano plots for DE gene at FDR = 0.05 between fresh and preserved tissue identified in the given cluster (blue) and DE genes identified in (A) for the same cluster (black). (C)(D) DE genes at FDR = 0.05 in cluster 4 between fresh and day 3 tissues. (Cluster ID and color for time followed that in Fig. 3A.) Figure S6. Number of gene sets enriched with FDR q value≤0.05 for genes that are (A) upregulated or (B) downregulated in cells from fresh tissues compared to those from preserved tissues. Figure S7. Number of genes with rejected null hypothesis by the Breusch-Pagan test at incrementing FDR for each identified cell cluster. Figure S8. Evaluation of gene expression variation between cells from fresh and preserved tissues via (A) dimension reduction on incrementing number of overdispersed genes (B) hierarchical clustering on top 500 over-dispersed genes (cluster ID follows that in Fig. 3A, cell clustering follows Fig. 2D). (PDF 6690 kb

Additional file 8: of High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis

Table S7. Gene ontology enrichment of genes from cluster3 with null hypothesis rejected at FDR = 0.05 by Breusch-Pagan test. (CSV 668 bytes

Additional file 4: of High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis

Table S3. Gene sets enriched with FDR q value ≤0.05 from GSEA. (CSV 732 bytes

Additional file 6: of High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis

Table S5. Output (top 20 hit) from GSEA on genes with negative log2(fold change) between cells from fresh and preserved tissues. (XLSX 104 kb

Additional file 7: of High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis

Table S6. Genes with null hypothesis rejected at FDR = 0.05 by Breusch-Pagan test. (XLSX 15 kb

Taxonomic and Functional Diversity Provides Insight into Microbial Pathways and Stress Responses in the Saline Qinghai Lake, China

<div><p>Microbe-mediated biogeochemical cycles contribute to the global climate system and have sensitive responses and feedbacks to environmental stress caused by climate change. Yet, little is known about the effects of microbial biodiversity (i.e., taxonmic and functional diversity) on biogeochemical cycles in ecosytems that are highly sensitive to climate change. One such sensitive ecosystem is Qinghai Lake, a high-elevation (3196 m) saline (1.4%) lake located on the Tibetan Plateau, China. This study provides baseline information on the microbial taxonomic and functional diversity as well as the associated stress response genes. Illumina metagenomic and metatranscriptomic datasets were generated from lake water samples collected at two sites (B and E). Autotrophic <i>Cyanobacteria</i> dominated the DNA samples, while heterotrophic <i>Proteobacteria</i> dominated the RNA samples at both sites. Photoheterotrophic <i>Loktanella</i> was also present at both sites. Photosystem II was the most active pathway at site B; while, oxidative phosphorylation was most active at site E. Organisms that expressed photosystem II or oxidative phosphorylation also expressed genes involved in photoprotection and oxidative stress, respectively. Assimilatory pathways associated with the nitrogen cycle were dominant at both sites. Results also indicate a positive relationship between functional diversity and the number of stress response genes. This study provides insight into the stress resilience of microbial metabolic pathways supported by greater taxonomic diversity, which may affect the microbial community response to climate change.</p></div

BCellClassSwitching

Scripts associated with Horns et al. Elife 2016. Available in GitHub at https://github.com/felixhorns/BCellClassSwitchin

Additional file 9: Figure S6. of Long-term microfluidic tracking of coccoid cyanobacterial cells reveals robust control of division timing

Growth behavior is similar across all chambers in light-dark cycle experiment. (a) Total growth in different chambers under light-dark cycles. Substantial growth is observed during illuminated periods across all microfluidic chambers. In the dark, minimal growth is detected. (b) Residual errors (gray, with mean shown in black) of exponential fits to lineage growth curves during the illumination periods L1 and L2 of Fig. 3a. (PDF 366 kb