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Next Generation Sequencing Facilitates Quantitative Analysis of TRAP from grafted human neural stem cells in stroke and naïve rat brains

ABSTRACT: Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to access grafted human neural stem cells and host tissue transcriptomes Methods: Grafted mRNA profiles of 7 days transplanted hNSC were generated by deep sequencing, in triplicate, using Illumina HiSeq 2500. The sequence reads that passed quality filters were analyzed at the transcript isoform level with Burrows–Wheeler Aligner (BWA) followed by RNA-Seq by Expectation Maximization (RSEM). qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped: 1) about 230 million sequence reads per sample to the human genome (build hg19) and identified 17,463 transcripts in the grafted hNSC; and 2) about 30 million sequence reads per sample to the rat genome (build rn5) and identified 13,4688 transcripts in the host tissues; using RSEM workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 4 housekeeping genes were validated with qPCR. RNA-seq data had a linear relationship with qPCR for more than four orders of magnitude. Approximately 14% of the transcripts showed differential expression between the grafted hNSC in naive and stroke; and approximately 41% of the transcripts showed differential expression between the naive and stroke tissues, with p value <0.05. Altered expression of over 10 genes was confirmed with qPCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to grafted hNSC and host function. Data analysis with TRAP and RSEM workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of grafted hNSC transcriptome, with biologic replicates, generated by TRAPseq technology. TRAPseq and RSEM could be applied to many xenograft cell transplantation paradigms. With this approach we can start to predict upstream regulators that signal between the host and graft, and to predict the downstream signaling pathways and biological processes these upstream regulators might affect.

ORGANISM(S): Rattus norvegicus Homo sapiens

PROVIDER: GSE150710 | GEO | 2023/02/08

REPOSITORIES: GEO

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Project description:Purpose: The purpose of this study was to simulataneously examine the host and fungal pathogen transcriptional profiles of four distinct infection fates during macrophage and Candida albicans interactions Methods: Membrane stained (Deep Red),primary, bone marrow derived, murine macrophages and Candida albicans expressing GFP and mCherry were exposed to each other over a four hour time course. Samples were collected at 0, 1, 2 and 4 hours and sorted for four infection subpopulations: 1. Macrophages which phagocytosed live C. albicans (GFP+ /mCherry+ /Deep Red +), 2. Macrophages which phagocytosed dead C. albicans (GFP- /mCherry+ /Deep Red +), 3. Uninfected macrophages(GFP- /mCherry- /Deep Red +) and 4. Unengulfed C. albicans (GFP+ /mCherry + /Deep Red -). Unexposed controls were also collected for some time points (i.e. macrophages never exposed to C. albicans and C. albicans never exposed to macrophages). Single macrophages infected with live or dead C. albicans were also sorted. Smart-seq2 was used to create libraries for both infection subpopulation and single, infected cell samples that were sequenced on Illumina’s Miseqand Nextseq. Basic quality assessment of Illumina reads and sample demultiplexing was done with Picard version 1.107 and Trimmomatic. Samples profiling exclusively the mouse transcriptional response were aligned to the mouse transcriptome generated from the v. Dec. 2011 GRCm38/mm10 and a collection of mouse rRNA sequences from the UCSC genome website. Samples profiling exclusively the yeast transcriptional response were aligned to the C. albicans transcriptome strain SC5314 version A21-s02-m09-r10 downloaded from Candida Genome Database. Samples containing both macrophages and C. albicans were aligned to a “composite transcriptome” made by combining the mouse transcriptome and C. albicans transcriptomes described above and alignment was done via BWA (version 0.7.10-r789.) Multi-reads (reads that aligned to both host and pathogen transcripts) were discarded. Then, each host or pathogen sample file were aligned to its corresponding reference using Bowtie2 and RSEM (v.1.2.21). Transcript abundance was estimated using transcripts per million (TPM). For subpopulation samples, TPM was calculated using edgeR, all as implemented in the Trinity package version 2.1.. Genes were considered differentially expressed only if they had a 4-fold change difference (> 4 FC) in TPM values and a false discovery rate below or equal to 0.001 (FDR < 0.001), unless specified otherwise. For single macrophages infected with C. albicans, samples were aligned to the combined transcriptome as described above and RSEM was used to calculate TPM. Results: We were able to simultaneously measure the host and fungal pathogen transcriptional profiles of four distinct infection fates during macrophage and Candida albicans interactions Conclusions: Our study represents an analysis of both distinct infection populations of macrophages and fungus.

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Next Generation Sequencing Facilitates Quantitative Analysis of TRAP from grafted human neural stem cells in stroke and naïve rat brains

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