Cell freezing protocol suitable for ATAC-Seq on motor neurons derived from human induced pluripotent stem cells

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.National Institutes of Health (U.S.) (Grants U54-NS-091046 and U01-CA-184898

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Understanding neurodegenerative disease-relevant molecular effects of perturbagens using a multi-omics approach

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019Cataloged from PDF version of thesis.Includes bibliographical references.The complex etiology of neurodegenerative diseases is not fully understood, and the characterization of cellular pathways that are dysfunctional in these diseases is key for therapeutic development. Chemical and genetic perturbagens can probe cellular pathways to shed insight about both disease etiology and potential therapeutic targets. We analyzed the functional effects of chemical perturbagens in neurodegenerative disease models as evidenced by changes in transcriptomic, metabolomic, epigenomic, and proteomic data ("multi-omics" data). Our studies revealed novel modes of action for small molecule compounds that promote survival in a model of Huntington's Disease, a fatal neurodegenerative disorder. Integration of our multi-omics data using an interpretable network approach revealed that the autophagy and bioenergetics cellular pathways are affected by different sets of compounds that promote survival. Using staining and western blot assays, we validated the effect on autophagy for one set of compounds and found that the compounds activate this pathway. Using a cellular bioenergetics assay, we found that a second set of compounds shifts the bioenergetic flux from mitochondrial respiration to glycolysis, validating our network results. In a second study related to Huntington's Disease, we analyzed the effects of two peripheral huntingtin gene silencing techniques in mouse liver. We show that the transcriptional and metabolomic changes associated with both genetic silencing methods converge on similar cellular pathways, such as the immune response and fatty acid metabolism. As a whole, this thesis presents new insights into the functional effects of perturbagens that could impact neurodegenerative disease pathology and drug discovery.by Natasha L. Patel-Murray.Ph. D.Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Progra

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules

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.National Institutes of Health (U.S.) (Grant R01 NS089076)National Institutes of Health (U.S.) (Grant U54 NS091046

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

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