Large-scale differentiation of iPSC-derived motor neurons from ALS and control subjects.

Using induced pluripotent stem cells (iPSCs) to understand the mechanisms of neurological disease holds great promise; however, there is a lack of well-curated lines from a large array of participants. Answer ALS has generated over 1,000 iPSC lines from control and amyotrophic lateral sclerosis (ALS) patients along with clinical and whole-genome sequencing data. The current report summarizes cell marker and gene expression in motor neuron cultures derived from 92 healthy control and 341 ALS participants using a 32-day differentiation protocol. This is the largest set of iPSCs to be differentiated into motor neurons, and characterization suggests that cell composition and sex are significant sources of variability that need to be carefully controlled for in future studies. These data are reported as a resource for the scientific community that will utilize Answer ALS data for disease modeling using a wider array of omics being made available for these samples.

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines.

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients.

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.

Generation of twenty four induced pluripotent stem cell lines from twenty four members of the Lothian Birth Cohort 1936.

Cognitive decline is among the most feared aspects of ageing. We have generated induced pluripotent stem cells (iPSCs) from 24 people from the Lothian Birth Cohort 1936, whose cognitive ability was tested in childhood and in older age. Peripheral blood mononuclear cells (PBMCs) were reprogrammed using non-integrating oriP/EBNA1 backbone plasmids expressing six iPSC reprogramming factors (OCT3/4 (POU5F1), SOX2, KLF4, L-Myc, shp53, Lin28, SV40LT). All lines demonstrated STR matched karyotype and pluripotency was validated by multiple methods. These iPSC lines are a valuable resource to study molecular mechanisms underlying individual differences in cognitive ageing and resilience to age-related neurodegenerative diseases.

A Comparison of mRNA Sequencing with Random Primed and 3'-Directed Libraries.

Creating a cDNA library for deep mRNA sequencing (mRNAseq) is generally done by random priming, creating multiple sequencing fragments along each transcript. A 3'-end-focused library approach cannot detect differential splicing, but has potentially higher throughput at a lower cost, along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) that correct PCR bias. Here, we compare an implementation of such a 3'-digital gene expression (3'-DGE) approach with "conventional" random primed mRNAseq. Given our particular datasets on cultured human cardiomyocyte cell lines, we find that, while conventional mRNAseq detects ~15% more genes and needs ~500,000 fewer reads per sample for equivalent statistical power, the resulting differentially expressed genes, biological conclusions, and gene signatures are highly concordant between two techniques. We also find good quantitative agreement at the level of individual genes between two techniques for both read counts and fold changes between given conditions. We conclude that, for high-throughput applications, the potential cost savings associated with 3'-DGE approach are likely a reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable many larger scale studies focusing on not only differential expression analysis, but also quantitative transcriptome profiling.