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Next generation sequencing of human immune cell subsets across diseases - GSE60424


This study compared whole transcriptome signatures of 6 immune cell subsets and whole blood from patients with an array of immune-associated diseases. Fresh blood samples were collected from healthy subjects and subjects diagnosed type 1 diabetes, amyotrophic lateral sclerosis, and sepsis, as well as multiple sclerosis patients before and 24 hours after the first treatment with IFN-beta. At the time of blood draw, an aliquot of whole blood was collected into a Tempus tube (Invitrogen), while the remainder of the primary fresh blood sample was processed to highly pure populations of neutrophils, monocytes, B cells, CD4 T cells, CD8 T cells, and natural killer cells. RNA was extracted from each of these cell subsets, as well as the whole blood samples, and processed into RNA sequencing (RNAseq) libraries (Illumina TruSeq). Sequencing libraries were analyzed on an Illumina HiScan, with a target read depth of ~20M reads. Reads were demultiplexed, mapped to human gene models (ENSEMBL), and tabulated using HTSeq. Read count data were normalized by the TMM procedure (edgeR package).

Experimental Design

We performed whole genome RNAseq profiling of immune cell subsets and whole blood from subjects with an array of immune-associated diseases.

Experimental Variables

Type 1 diabetes (2021 ICD-10-CM code* = E10)

Amyotrophic lateral sclerosis (2021 ICD-10-CM code* = G12.21)

Sepsis (2021 ICD-10-CM code* = A41)

Multiple sclerosis (2021 ICD-10-CM code* = G35)


fastq files were created from .bcl files via Illumina's CASAVA 1.8.2

reads were aligned to Hg19 via Omicsoft sequence aligner (OSA) version 2.0.1

gene counts were generated by HTSeq version 0.5.4p3

TMMs were generated with Bioconductor package EdgeR

Genome_build: Hg19

Additional Information

Platform Illumina HiScan
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