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.

In recent years, the assay for transposase-accessible chromatin using sequencing (ATAC-Seq) has become a fundamental tool of epigenomic research. However, it is difficult to perform this technique on frozen samples because freezing cells before extracting nuclei can impair nuclear integrity and alter chromatin structure, especially in fragile cells such as neurons. Our aim was to develop a protocol for freezing neuronal cells that is compatible with ATAC-Seq; we focused on a disease-relevant cell type, namely motor neurons differentiated from induced pluripotent stem cells (iMNs) from a patient affected by spinal muscular atrophy. We found that while flash-frozen iMNs are not suitable for ATAC-Seq, the assay is successful with slow-cooled cryopreserved cells. Using this method, we were able to isolate high quality, intact nuclei, and we verified that epigenetic results from fresh and cryopreserved iMNs quantitatively agree.Since its establishment, the assay for transposase-accessible chromatin using sequencing (ATAC-Seq) has revolutionized the study of epigenetics 1,2 . This technique detects open-chromatin regions and maps transcription factor binding events genome-wide by means of direct in vitro transposition of native chromatin. Specifically, hyperactive Tn5 transposase is used to interrogate chromatin accessibility by inserting high-throughput DNA sequencing adapters into open genomic regions, which allows for the preferential amplification of DNA fragments located at sites of active chromatin. Because the DNA sites directly bound by DNA-binding proteins are protected from transposition, this approach enables the inference of transcription factor occupancy at the level of individual functional regulatory regions. Furthermore, ATAC-Seq can be utilized to decode nucleosome occupancy and positioning, by exploiting the fact that the Tn5 transposase cuts DNA with a periodicity of about 150-200 bp, corresponding to the length of the DNA fragments wrapped around histones 3 . This periodicity is maintained up to six nucleosomes and provides information about the spatial organization of nucleosomes within accessible chromatin. ATAC-Seq signals thus allow for the delineation of fine-scale architectures of the regulatory framework by correlating occupancy patterns with other features, such as chromatin remodeling and global gene induction programs. Compared to other epigenetic methodologies, such as FAIRE-Seq and conventional DNase-Seq, ATAC-Seq requires a small number of cells. Therefore, it is suitable for work on precious samples, including differentiated cells derived from induced pluripotent stem cells (iPSCs), primary cell culture, and limited clinical specimens. Recently developed techniques, such as single-cell DNase sequencing (scDNase-seq) 4 , indexing-first ChIP-Seq (iChIP) 5 , ultra-low-input micrococcal nuclease-based native ChIP (ULI-NChIP) 6 , and ChIPmentation 7 , allow for the epigenomic investigation of small number of cells or even single cells without requiring microfluidic devices. However, these ...

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. examining compounds with beneficial effects in models of Huntington's Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. the approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. the use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases. Unknown modes of action of drug candidates can lead to unpredicted consequences on effectiveness and safety. Computational methods, such as the analysis of gene signatures, and high-throughput experimental methods have accelerated the discovery of lead compounds that affect a specific target or phenotype 1-3. However, these advances have not dramatically changed the rate of drug approvals. Between 2000 and 2015, 86% of drug candidates failed to earn FDA approval, with toxicity or a lack of efficacy being common reasons for their clinical trial termination 4,5. Even compounds identified for binding to a specific target can have complex downstream functional consequences, or modes of action (MoAs) 6. Understanding the MoAs of compounds remains a crucial challenge in increasing the success rate of clinical trials and drug repurposing efforts 4,6. Computational approaches have contributed to the discovery of MoAs. Using the Connectivity Map data, tools like MANTRA can predict MoAs of new compounds based on their gene expression similarity to reference compounds with known MoAs 7. To combat antibiotic resistance, reference compounds were also used to infer MoAs of uncharacterized antimicrobial compounds by comparing their untargeted metabolomic profiles in bacteria 8. From human cancer cell lines, basal gene expression signatures were correlated with sensitivity patterns of compounds to identify previously unknown activation mechanisms and compound binding targets 9. Similarly, gene expression profiles of human lymphoma cells treated with anti-cancer drugs were compared using the gene regulatory network-based DeMAND algorithm to predict novel targets and unexpected similarities between the drugs 10. However, all of these methods require prior context-specific knowledge, such as data from reference compounds with known MoAs, sensitivity data, or gene-regulatory interactions. More general approaches to discover MoAs are urgently needed. In...

HighlightsMulti-omic analysis of differentiated C9ORF72 iPSC-derived motor neurons Network-based integrative computational analysis Pathogenic versus compensatory pathways elucidated using C9ORF72 Drosophila model Pathways confirmed with alternative differentiation protocol and postmortem data

Neurodegenerative diseases present a challenge for systems biology, due to the lack of reliable animal models and the difficulties in obtaining samples from patients at early stages of disease, when interventions might be most effective. Studying induced pluripotent stem cell (iPSC)-derived neurons could overcome these challenges and dramatically accelerate and broaden therapeutic strategies. Here we undertook a network-based multi-omic characterization of iPSC-derived motor neurons from ALS patients carrying genetically dominant hexanucleotide expansions in C9orf72 to gain a deeper understanding of the relationship between DNA, RNA, epigenetics and protein in the same pool of tissue. ALS motor neurons showed the expected C9orf72-related alterations to specific nucleoporins and production of dipeptide repeats. RNA-seq, ATAC-seq and data-independent acquisition mass-spectrometry (DIA-MS) proteomics were then performed on the same motor neuron cultures. Using integrative computational methods that combined all of the omics, we discovered a number of novel dysregulated pathways including biological adhesion and extracellular matrix organization and disruption in other expected pathways such as RNA splicing and nuclear transport. We tested the relevance of these pathways in vivo in a C9orf72 Drosophila model, analyzing the data to determine which pathways were causing disease phenotypes and which were compensatory. We also confirmed that some pathways are altered in late-stage neurodegeneration by analyzing human postmortem C9 cervical spine data. To validate that these key pathways were integral to the C9 signature, we prepared a separate set of C9orf72 and control motor neuron cultures using a different differentiation protocol and applied the same methods. As expected, there were major overall differences between the differentiation protocols, especially at the level of in individual omics data. However, a number of the core dysregulated pathways remained significant using the integrated multiomic analysis. This new method of analyzing patient specific neural cultures allows the generation of disease-related hypotheses with a small number of patient lines which can be tested in larger cohorts of patients.